WO2019052432A1 - 数据传输方法、移动终端及计算机可读存储介质 - Google Patents

数据传输方法、移动终端及计算机可读存储介质 Download PDF

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Publication number
WO2019052432A1
WO2019052432A1 PCT/CN2018/104923 CN2018104923W WO2019052432A1 WO 2019052432 A1 WO2019052432 A1 WO 2019052432A1 CN 2018104923 W CN2018104923 W CN 2018104923W WO 2019052432 A1 WO2019052432 A1 WO 2019052432A1
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WIPO (PCT)
Prior art keywords
image
clustered
mobile terminal
server
clustering
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PCT/CN2018/104923
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English (en)
French (fr)
Inventor
柯秀华
曹威
王俊
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Oppo广东移动通信有限公司
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Publication of WO2019052432A1 publication Critical patent/WO2019052432A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/51—Indexing; Data structures therefor; Storage structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/94—Hardware or software architectures specially adapted for image or video understanding
    • G06V10/95—Hardware or software architectures specially adapted for image or video understanding structured as a network, e.g. client-server architectures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation

Definitions

  • the present application relates to the field of Internet technologies, and in particular, to a data transmission method, a mobile terminal, and a non-transitory computer readable storage medium.
  • a data transmission method, a mobile terminal, and a non-transitory computer readable storage medium are provided in accordance with various embodiments of the present application.
  • a data transmission method is applied to a mobile terminal, including:
  • the clustering result includes an image identifier to be clustered corresponding to the image feature, and a label allocated after clustering the image feature;
  • a mobile terminal includes a memory and a processor, wherein the memory stores computer executable instructions, and when the computer executable instructions are executed by the processor, the processor performs the following operations:
  • the clustering result includes an image identifier to be clustered corresponding to the image feature, and a label allocated after clustering the image feature;
  • One or more non-transitory computer readable storage media containing computer executable instructions that, when executed by one or more processors, cause the processor to:
  • the clustering result includes an image identifier to be clustered corresponding to the image feature, and a label allocated after clustering the image feature;
  • the clustered image is identified, the image features of the image to be clustered are extracted, the image features are uploaded to the server for clustering, and only the image features are uploaded to complete the image clustering, without uploading the whole
  • the image can reduce the amount of data transferred and increase the transmission speed.
  • the image to be clustered can be assigned to the corresponding category according to the clustering result returned by the server, and the image is classified and displayed, which is easier to find and convenient.
  • 1 is an application scenario diagram of a data transmission method in an embodiment
  • FIG. 2 is a sequence diagram of interaction between a mobile terminal and a server in an embodiment
  • FIG. 3 is a flow chart of a data transmission method in an embodiment
  • FIG. 5 is a flow chart of comparing images stored in a first database and a second database in one embodiment
  • FIG. 6 is a flow chart of uploading image features to a server in one embodiment
  • Figure 7 is a block diagram of a data transmission device in an embodiment
  • Figure 8 is a block diagram of an acquisition module in one embodiment
  • FIG. 9 is a block diagram of an upload module in one embodiment
  • Figure 10 is a block diagram of a mobile terminal in one embodiment.
  • first may be referred to as a second client
  • second client may be referred to as a first client, without departing from the scope of the present application.
  • Both the first client and the second client are clients, but they are not the same client.
  • FIG. 1 is an application scenario diagram of a data transmission method in an embodiment.
  • the mobile terminal 10 can establish a communication connection with the server 20 through a network, wherein the server 20 can be a single server, a server cluster composed of multiple servers, or one of the server clusters. Server.
  • the mobile terminal 10 acquires an image to be clustered, recognizes the image to be clustered, and extracts image features of the image to be clustered.
  • the mobile terminal 10 uploads the image features of the image to be clustered to the server 20, and the server 20 clusters the image to be clustered according to the image features of the image to be clustered uploaded by the mobile terminal 10, and returns the clustering result to the mobile terminal 10.
  • the mobile terminal 10 receives the clustering result returned by the server 20, and the clustering result may include an image identifier to be clustered corresponding to the image feature, and a label allocated after clustering the image feature.
  • the mobile terminal 10 may assign the image to be clustered to the group corresponding to the label according to the corresponding image to be clustered and the assigned label.
  • FIG. 2 is a timing diagram of interaction between a mobile terminal and a server in one embodiment. As shown in FIG. 2, the main interaction process between the mobile terminal and the server may include the following operations:
  • the mobile terminal 10 identifies the cluster image and extracts image features of the image to be clustered.
  • the mobile terminal 10 may acquire one or more images to be clustered, and identify each acquired image to be clustered, and extract image features of the image to be clustered.
  • the mobile terminal 10 may analyze the clustered image by using a preset face recognition model to determine whether a corresponding face is included in the image to be clustered.
  • the facial feature points of the image to be clustered may be extracted, wherein the facial feature points may be used to describe the shape of the face, the shape of the facial features, and the position.
  • the mobile terminal 10 uploads image features to the server 20.
  • the mobile terminal 10 may upload the extracted image features of the image to be clustered to the server 20, and the image features may include corresponding image information, wherein the image information may include an image identifier to be clustered corresponding to the image features, and the image to be clustered
  • the identifier may be information such as the name or number of the image to be clustered.
  • the mobile terminal 10 may extract current image group information and image features of the grouped images in each group, wherein the image group information may include group information for each group, and each group includes Image information, etc.
  • the mobile terminal may pack the image grouping information, the image features of the grouped image in each group, and the image features of the image to be clustered into an uplink data packet, and send the uplink data packet to the server 20.
  • the server 20 clusters the clustered images and assigns corresponding labels.
  • the mobile terminal 10 may send a clustering request to the server 20, and the server 20 may cluster the image features of the clustered image according to the clustering request of the mobile terminal 10.
  • the clustering request may include information about the identity, account, and sending time of the corresponding mobile terminal 10, and the server 20 may add the clustering request to the queue service after receiving the clustering request.
  • the server 20 performs the clustering request allocation, the clustering requests sent by the same mobile terminal at different sending moments in the queue service may be combined, or the clustering requests sent by the same account in the queue service may be combined.
  • the server 20 may parse the uplink data packet, obtain image group information, image features of the grouped image in each group, and image features of the image to be clustered, and the like. information.
  • the server 20 may calculate a similarity between the image features of the image to be clustered and the image features of the grouped images in each group by using a preset clustering model, and determine a group to which the image to be clustered corresponds to the image feature belongs to, and Assign the corresponding label.
  • the server 20 returns the clustering result to the mobile terminal 10.
  • the mobile terminal 10 assigns the image to be clustered to the group corresponding to the label according to the image to be clustered and the assigned label corresponding to the image feature in the clustering result.
  • the server 20 may return the clustering result to the mobile terminal 10.
  • the clustering result may include an image identifier to be clustered corresponding to the image feature and a label allocated after clustering the image feature.
  • the mobile terminal 10 may add each image to be clustered to the corresponding group according to the clustering result, and obtain, from the clustering result, the image to be clustered corresponding to the image feature and the assigned label, which will correspond to the image feature.
  • the image to be clustered is assigned to the group corresponding to the tag, and the corresponding group identifier is assigned.
  • a data transmission method including the following operations:
  • Operation 310 Acquire an image to be clustered of the mobile terminal.
  • the mobile terminal may acquire one or more images to be clustered from a memory such as a memory.
  • the image to be clustered may be an image taken by the user on the mobile terminal, or may be an image acquired from another computer device.
  • the image to be clustered may be an image sent by another mobile terminal, or may be an image saved when the user browses the webpage through the mobile terminal.
  • the image to be clustered may be a photo, and the mobile terminal may cluster the clustered image by the server to generate a corresponding album.
  • the image to be clustered may be an image without a group stored on the mobile terminal, that is, an image that is not clustered, or an image that has a corresponding group but needs to be re-clustered. Wait.
  • the image to be clustered may be an image that is not grouped and stored on the mobile terminal.
  • a corresponding group identifier is assigned, and the mobile terminal can obtain a corresponding group in the stored image. The identified image as the image to be clustered.
  • the mobile terminal may detect whether the acquired plurality of images to be clustered include repeated images to be clustered, wherein the repeated image to be clustered refers to The plurality of images to be clustered with the similarity greater than the first threshold, when the acquired plurality of images to be clustered include repeated images to be clustered, the mobile terminal may select the highest quality to be clustered from the plurality of repeated images to be clustered. The image is identified, and the image features of the highest quality image to be clustered are extracted for uploading. The mobile terminal can determine the image quality according to the values of saturation, sharpness, brightness, etc. in the repeated images to be clustered, and select the image with the highest quality to be clustered for recognition.
  • Operation 320 identifying the image to be clustered, and extracting image features of the image to be clustered.
  • the mobile terminal may identify the acquired images to be clustered and extract image features of the image to be clustered.
  • the server can cluster images based on faces.
  • the mobile terminal may first perform face recognition on each image to be clustered, and divide the image to be clustered into an unmanned image and a face image. Further, the mobile terminal may analyze the cluster image by using a preset face recognition model, and determine whether the corresponding image to be clustered includes a face.
  • the face recognition model may be a decision model constructed in advance through machine learning.
  • the sample image When constructing the face recognition model, a large number of sample images may be acquired, and the sample image includes a face image and an unmanned image, which may be according to each Whether the sample image contains a human face marks the sample image, and the marked sample image is used as an input of the face recognition model, and is trained by machine learning to obtain a face recognition model.
  • the mobile terminal may extract only the image features of the face image in the image to be clustered and perform clustering according to the image features of the face image.
  • the mobile terminal may extract image features of each face image according to a preset feature model, and the image features may include shape features, spatial features, edge features, etc., wherein the shape features refer to local shapes and spatial features in the image to be clustered. Refers to the mutual spatial position or relative direction relationship between multiple regions segmented in the image to be clustered.
  • the edge feature refers to the boundary pixel between the two regions in the image to be clustered, but is not limited thereto. It can also contain color features, texture features, and the like. Further, the mobile terminal performs face recognition on the cluster image, and when detecting that the image to be clustered includes a face, the face feature points of the image to be clustered may be extracted, wherein the face feature point may be used to describe the face shape. , five features and location.
  • Operation 330 uploading image features to the server.
  • the mobile terminal may upload the extracted image features of the image to be clustered to the server, and the image features may include corresponding image information, where the image information may include image identifiers to be clustered corresponding to the image features, to be clustered
  • the image identifier may be information such as the name or number of the image to be clustered.
  • the server may cluster the clustered images according to the image features of the images to be clustered, and classify the images to be clustered including the similar image features into one category.
  • the server may perform clustering on the cluster image according to the face, and the server may have a feature point that is uploaded by the mobile terminal and can be used to describe the shape of the face and the shape, position, and the like in the image to be clustered.
  • the images to be clustered with similar face feature points are classified into one class.
  • the server may add a corresponding label to the image feature of each image to be clustered according to the clustering result, and the label may be used to represent the group to which the image to be clustered corresponding to the image feature belongs.
  • the clustering result returned by the server is received, and the clustering result includes an image identifier to be clustered corresponding to the image feature, and a label allocated after clustering the image feature.
  • the image to be clustered is allocated to the group corresponding to the label according to the corresponding image to be clustered and the assigned label.
  • the server may return the clustering result to the mobile terminal.
  • the clustering result may include an image identifier to be clustered corresponding to the image feature and a label allocated after clustering the image feature.
  • the mobile terminal may add each image to be clustered to the corresponding group according to the clustering result, and obtain the image to be clustered and the assigned label corresponding to the image feature from the clustering result, and the corresponding to the image feature.
  • the cluster image is assigned to the group corresponding to the tag, and the corresponding group identifier is assigned.
  • the mobile terminal may create one or more albums, and each group may correspond to one album respectively, and images belonging to the same group may be displayed in the same album.
  • the data transmission method obtains the image to be clustered of the mobile terminal, identifies the cluster image, extracts the image feature of the image to be clustered, uploads the image feature to the server for clustering, and only uploads the image feature to complete the image clustering. , without uploading the entire image, can reduce the amount of data transmitted and increase the transmission speed.
  • the image to be clustered can be assigned to the corresponding category according to the clustering result returned by the server, and the image is classified and displayed, which is easier to find and convenient.
  • operation 310 acquires an image to be clustered of the mobile terminal, including the following operations:
  • Operation 402 Align the image information stored in the first database and the second database, and generate at least one of a new image list and an updated image list according to the comparison result.
  • the first database refers to a media database
  • the media database can be used to store information of multimedia files such as images, videos, audios, etc., and can be used by a video player, an audio player, and an album library.
  • the first database may include a storage path of the image, a message digest, a multimedia number, a modification time, and the like, for storing information of the image.
  • the first database may include an SD card (Secure Digital Memory Card) media database and a memory media database, wherein the SD card media database may be used to store multimedia information of the SD card, and the memory media database may be used for storing in the memory. Multimedia information.
  • SD card Secure Digital Memory Card
  • the second database refers to a face database, and a face recognition scan result, an image feature, a group information, and the like of each image may be stored in the face database.
  • the face database may include multiple types of fields, such as a picture attribute, a face attribute, and a group attribute.
  • the picture attribute may include an image storage path, a message digest, a multimedia number, a modification time, and the like
  • the face attribute may include
  • the group attribute may include a group identifier, a group name, a creation time, and the like, but is not limited thereto.
  • the mobile terminal When the mobile terminal collects a new image, for example, can be collected by a camera or received from another computer device, the mobile terminal needs to store the image in the first database; when performing face recognition scanning on the image, After the image features are extracted and clustered according to the image features, the information of the image, the corresponding image features, group information, and the like may be stored in the face database.
  • clustering may be performed according to other features, such as scenes, places, or times, etc.
  • the second database may be stored with feature information for clustering.
  • the database of information such as clustering results is not limited to the face database described above.
  • the mobile terminal may compare the image stored in the first database with the image information stored in the second database, and may perform comparison according to a storage path, a multimedia number, a modification time, or a message summary, and generate a new image list and/or Or update the image list.
  • the newly added image list may record an image in the mobile terminal that is not face-recognized, and the mobile terminal may add an image existing in the first database but not in the second database to the newly added image list.
  • the updated image list may record an image in which the content is changed after face recognition, and may be added to the updated database list or the like in the first database and the second database.
  • the mobile terminal may generate only one of a new image list and an updated image list, or may simultaneously generate a new image list and an updated image list.
  • Operation 404 determining an image to be clustered according to at least one of a new image list and an updated image list.
  • the mobile terminal may directly use the image included in the newly added image list and/or the updated image list as the image to be clustered, and extract the image features of the image to be clustered to the server for clustering.
  • the mobile terminal may determine whether there is an image in the updated image list that has a corresponding group but needs to be re-clustered, and may extract an image feature of each image in the updated image list. The extracted image features are compared to corresponding image features stored in the second database.
  • the mobile terminal may determine that the image whose similarity is greater than the preset value may not be re-clustered; when the extracted image When the similarity between the feature and the corresponding image feature stored in the second database is less than a preset value, the mobile terminal may determine that the image whose similarity is less than the preset value needs to be re-clustered.
  • the mobile terminal may use the newly added image list and the image in the updated image list that needs to be re-clustered as the image to be clustered.
  • the server can acquire the image to be clustered, and can only cluster the images that need to be clustered, which can reduce the pressure of the server and improve the efficiency of image clustering.
  • operation 402 compares the image information stored in the first database and the second database, and generates a new image list and/or an updated image list according to the comparison result, including the following operations:
  • Operation 502 determining, according to the path of the image in the first database, whether the corresponding image is found in the second database. When the corresponding image is found in the second database, performing operation 506, when there is no search in the second database. When the corresponding image is reached, operation 504 is performed.
  • the mobile terminal may search in the second database according to the path of the image in the first database, and determine whether the face recognition result corresponding to the image is stored in the second database.
  • the mobile terminal can read the value of each image stored in the first database in the storage path field one by one, and find whether the second database has an image in which the value of the storage path field is consistent with the read value, and when occasionally, the second database
  • the image in which the value of the storage path field is consistent with the read value is the corresponding image in the second database.
  • the mobile terminal may also search for a corresponding image in the second database according to the multimedia number of each image in the first database, and when the second database can find the image with the multimedia number consistent with the first database.
  • the image with the same multimedia number is the corresponding image in the second database.
  • Operation 504 adding an image that is not found to the newly added image list.
  • the mobile terminal does not find the corresponding image in the second database according to the path of the image in the first database
  • the image exists only in the first database and not in the second database, indicating that the image is not performed.
  • the face recognition scan can add an image in the first database that does not find the corresponding image in the second database to the newly added image list.
  • the newly added image list may record identification information of an image existing only in the first database and not in the second database, wherein the identification information may be a multimedia number, a storage path, or the like.
  • operation 506 it is determined whether the image in the first database is consistent with the modification time of the corresponding image in the second database. When they are consistent, operation 512 is performed, and when they are inconsistent, operation 508 is performed.
  • the value of the modified time field of the image in the first database is extracted, and the value of the modified time field of the corresponding image in the second database is determined, and whether the two are Consistently, when the modification time is consistent, the image is not subjected to processing after the face recognition is performed and stored in the second database without modification.
  • operation 508 it is determined whether the image in the first database is consistent with the message digest of the corresponding image in the second database. When they are consistent, operation 512 is performed, and when they are inconsistent, operation 510 is performed.
  • the modification time of the image in the first database is inconsistent with the modification time of the corresponding image in the second database
  • the description image is performed after the face recognition is performed and stored in the second database, and the mobile terminal is modified.
  • the value of the message digest field stored in the first database and the value of the message digest field of the corresponding image in the second database may be further extracted and compared for consistency.
  • the message digest may also be referred to as a digital digest.
  • Each message digest is a fixed-length value that uniquely corresponds to a message or text, etc., by determining whether the image in the first database is consistent with the message digest of the corresponding image in the second database. It can be judged whether the content of the image has changed.
  • the message digest is inconsistent, the image is changed after the face recognition scan is performed and stored in the second database, and the image stored in the first database and the second database are changed.
  • the corresponding image in the image is not the same content.
  • the message digest image may be an image of MD5 (Message Digest Algorithm MD5, fifth edition message digest algorithm), and may be another hash algorithm is not limited thereto.
  • MD5 Message Digest Algorithm MD5, fifth edition message digest algorithm
  • the message digest of the image is calculated according to an algorithm such as MD5, and the message digest is stored in association with the multimedia number of the image, the storage path, and the like. In the database.
  • Operation 510 adding an image with inconsistent message digest to the updated image list.
  • the mobile terminal may add, in the first database, an image different from the message digest of the corresponding image in the second database to the updated image list, and the updated image list may record that the content has changed after the face recognition is performed. Further, the image may be recorded with identification information of the image whose content has changed after the face recognition scan.
  • the modification time of the image in the first database is different from the modification time of the second database, but the message digest is the same, it indicates that the image is modified after the face recognition, but the mobile terminal does not change the image content. Process it.
  • the first database and the second database may be compared, and a new image list and/or an updated image list may be generated to facilitate determining an image that needs to be clustered, so that only images that need to be clustered are clustered. Can reduce the pressure on the server and improve the efficiency of image clustering.
  • operation 330 uploads image features to the server, including the following operations:
  • Operation 602 extracting current image group information and image features of the grouped images in each group.
  • the mobile terminal may extract current image group information, where the image group information may include group information of each group, such as group identification, group name, creation time, and the like, and may also include each group Image information, such as identification information of an included image, a storage path, and the like.
  • the image grouping information may be expressed in the form of group_id:pic_id, where group_id represents the group identification and pic_id represents the multimedia number of the image.
  • the mobile terminal may extract current image group information from the second database and buffer the image group information into the third database.
  • the third database refers to a backup database, which can be used to store information that interacts with the server, such as information sent to the server, and information sent by the server.
  • a plurality of types of fields such as an image attribute, a face attribute, and a group attribute, may be included, and the number of fields under each attribute may be less than that of the second database, and only the fields related to the interaction with the server are retained, for example,
  • the image attribute may include only a storage path, a multimedia number, and the like.
  • the face attribute may only include fields such as a face feature
  • the group attribute may include only a group identifier, a creation time, and the like, but is not limited thereto.
  • the current image grouping information may include manually grouped grouping information and automatically clustered grouping information, wherein the manually grouped grouping information refers to grouping information manually grouped by a user, including user-created groupings.
  • the grouping information of the automatically clustered group, the group to which the manually adjusted photo belongs, and the like, the grouping information of the automatic clustering refers to a group generated by clustering according to the image features of the respective images, such as a server or a mobile terminal.
  • the grouped image in each group may be extracted from the second database according to the image grouping information.
  • the image feature may extract image features of each image included in each group from the second database and store them in the third database.
  • the image features of the grouped images in each group are extracted, and the image features corresponding to the respective groups, for example, the face features corresponding to each group, can be determined, which can help the server to cluster the image features of the cluster images.
  • the image grouping information, the image features of the grouped images in each group, and the image features of the image to be clustered are packaged into an uplink data packet.
  • the mobile terminal may package the image group information, the image feature of the grouped image in each group, and the image feature of the image to be clustered into an uplink data packet according to a preset format, and upload the uplink data packet to the server for image aggregation. class.
  • the mobile terminal may package the image features of the images belonging to the same group into the same uplink data packet according to the group, and carry group information such as the group identifier and the group name of the corresponding group.
  • Operation 606 uploading the uplink data packet to the server.
  • Operation 608 sending a clustering request to the server, the clustering request instructing the server to calculate the similarity between the image features of the image to be clustered and the image features of the grouped images in each group, determine the group of image features, and assign corresponding labels.
  • the mobile terminal may upload the uplink data packet to the server and send a clustering request to the server.
  • the server may be a single server or a distributed server cluster composed of multiple hosts.
  • the server cluster may include multiple servers, and each server may provide image clustering services to the mobile terminal. .
  • the server cluster may add the clustering request to the queue service, and allocate a clustering request to each server in the server cluster according to the queue service, and perform an image by the server allocated to the clustering request. Clustering.
  • Each clustering request included in the queue service may carry information such as an identifier, an account, and a sending time of the corresponding mobile terminal that is sent, where the identifier of the mobile terminal may be a MAC of the mobile terminal (Media Access Control, medium access control) Layer) address, or IMSI (International Mobile Subscriber Identification Number).
  • identifier of the mobile terminal may be a MAC of the mobile terminal (Media Access Control, medium access control) Layer) address, or IMSI (International Mobile Subscriber Identification Number).
  • the server cluster may allocate a clustering request to the server of the server cluster according to the order in which the clustering requests are sent in the queue service.
  • the server cluster may detect whether the queue service includes a clustering request that is sent by the same mobile terminal at the same time as the allocated clustering request, and when included, may belong to the assigned clustering request.
  • the clustering requests sent by the same mobile terminal at different sending moments are merged, and the merged clustering request is allocated to the server. For example, the currently allocated clustering request is sent by the mobile terminal A at 6:00 on August 2, 2017, and the detected queue service further includes the cluster that the mobile terminal A sends at 7:00 on August 2, 2017.
  • the server cluster may merge the above three clustering requests belonging to the mobile terminal A, and merge the clustering requests. Assigned to the server for unified processing by the server.
  • it may also detect whether the queue service includes a clustering request that is sent by the same account as the allocated clustering request, and when included, may be sent to the same account as the allocated clustering request.
  • the clustering request is merged and the merged clustering request is assigned to the server.
  • the currently assigned clustering request is sent by the account X through the mobile terminal A at 6:00 on August 2, 2017, and it is detected that the queue service also includes the account X through the mobile terminal B on August 2, 2017 7
  • the server cluster may merge the two clustering requests belonging to the account X as described above, and allocate the merged clustering request to the server, and the server performs unified processing.
  • the server may parse the uplink data packet, and obtain information such as image grouping information, image features of the grouped image in each group, and image features of the image to be clustered.
  • the server clusters each image to be clustered by a preset clustering model. Further, the server may separately calculate the similarity with the image features of the grouped images in each group by using the clustering model for the image features of each image to be clustered.
  • the server may be considered to belong to the same type of image, and the server may assign the image feature to the group with the similarity greater than the second threshold.
  • a correspondence between the label and the corresponding image to be clustered may be established.
  • the preset cluster model may not belong to the existing group.
  • the image features of the group to be clustered are clustered again, and the images to be clustered with similar image features are segmented to generate new groups, and the images to be clustered corresponding to the image features belonging to the same new group Assign the corresponding label.
  • the server when the server receives the merged clustering request, the server directly acquires image features of all the images to be clustered corresponding to the merged clustering request, and performs clustering to improve the image. Clustering efficiency.
  • the server may formulate a clustering strategy according to actual needs, determine whether to cluster only the clustered images, or cluster all images in which the image features of the mobile terminal are uploaded. For example, when the server performs the clustering model update, a clustering strategy for clustering all images in which the image features of the mobile terminal are uploaded may be formulated, wherein when all the images with historical image features are clustered, Groups with manual grouping attributes are retained, and clustering is performed again for groups and images that do not involve manual operations by the user.
  • the mobile terminal may add the corresponding image to be clustered to the group matching the label according to the image to be clustered corresponding to the image feature and the assigned label, and gather The class result is cached in the third database.
  • the mobile terminal may update the second database according to the clustering result returned by the server cached in the third database.
  • the clustering images may be grouped according to the existing grouping information and the image features of the grouped images in each group, so that the clustering result is more accurate, and the actual needs of the user are improved, and the image is improved.
  • the efficiency of clustering can increase the viscosity of the user.
  • the operation before acquiring the image to be clustered of the mobile terminal, the operation may include: acquiring a current power state, and when the power state meets the preset state, performing operation 310 to acquire a to-be-cluster of the mobile terminal. image.
  • the mobile terminal may acquire the current power state before uploading the image feature of the image to be clustered to the server, where the power state may include the available remaining power, whether it is in the charging state, the power consumption speed, and the like.
  • the power state meets the preset state
  • the image to be clustered is acquired, the cluster image is identified, the image feature of the image to be clustered is extracted, and the image feature is uploaded to the server.
  • the preset state may be that the available remaining power is greater than the preset percentage, or is in the charging state, or the available remaining power is greater than the preset percentage and the power consumption is less than the set value, etc., and is not limited thereto, and may be set according to actual needs. .
  • the mobile terminal may preset an uploading time period for uploading the image feature of the image to be clustered, and when the current time is in the preset uploading time period, uploading the image feature of the image to be clustered to the server .
  • the uploading period can be set in a period in which the mobile terminal is used less, for example, from 2 am to 4 am in the morning.
  • the state of the power source of the mobile terminal when uploading the image feature of the image to be clustered is ensured, and the feature of the uploaded image is reduced. The impact of the use of the terminal.
  • a data transmission method including the following operations:
  • Operation (2) when the power state meets the preset state, acquiring the image to be clustered of the mobile terminal.
  • Operation (4) extracting current image grouping information and image features of the grouped images in each group, and packaging the image grouping information, the image features of the grouped images in each group, and the image features of the image to be clustered into uplink data. package.
  • Operation (5) upload the uplink data packet to the server.
  • Operation (6) sending a clustering request to the server, the clustering request instructing the server to calculate the similarity between the image features of the image to be clustered and the image features of the grouped images in each group, and determining the group of each image to be clustered and Assigning a corresponding label, where the clustering request includes a mobile terminal identifier and a sending time, and when the clustering request is allocated to the server, the indication server acquires, according to the mobile terminal identifier, a cluster that belongs to the same mobile terminal and sent at different sending moments.
  • the clustering request includes account information, and when the clustering request is allocated to the server, the server is instructed to acquire the clustering request sent by the same account according to the account information, and the clustering request is obtained. Consolidate.
  • the clustering result includes an image identifier to be clustered corresponding to the image feature, and a label allocated after clustering the image feature.
  • the image to be clustered is allocated to the group corresponding to the label according to the corresponding image to be clustered and the assigned label.
  • the image to be clustered is acquired, the image to be clustered is identified, the image features of the image to be clustered are extracted, the image features are uploaded to the server for clustering, and only the image features are uploaded to complete the image clustering. Uploading the entire image can reduce the amount of data transferred and increase the transfer speed.
  • the operations in the flowchart of the method of the embodiment of the present application are sequentially displayed in accordance with the indication of the arrows, but the operations are not necessarily performed in the order indicated by the arrows. Except as explicitly stated herein, the execution of these operations is not strictly limited, and may be performed in other sequences. Moreover, at least a part of the operations in the method flowchart of the embodiment of the present application may include multiple sub-operations or multiple stages, which are not necessarily performed at the same time, but may be executed at different times. The order of execution is not necessarily performed sequentially, but may be performed alternately or alternately with at least a portion of the sub-operations or phases of other operations or other operations.
  • a data transmission apparatus 700 including an acquisition module 710, an extraction module 720, an uploading module 730, a receiving module 740, and an allocating module 750.
  • the obtaining module 710 is configured to acquire an image to be clustered of the mobile terminal.
  • the extracting module 720 is configured to identify the image to be clustered, and extract image features of the image to be clustered.
  • the extraction module 720 is further configured to perform face recognition on the clustered image, and when detecting that the image to be clustered includes a human face, extract a facial feature point of the image to be clustered, and use the facial feature point for the facial feature point. Describe the shape of the face, the shape of the facial features and the position.
  • the uploading module 730 is configured to upload image features to the server.
  • the receiving module 740 is configured to receive a clustering result returned by the server, where the clustering result includes an image identifier to be clustered corresponding to the image feature, and a label allocated after clustering the image feature.
  • the allocating module 750 is configured to allocate the image to be clustered to the group corresponding to the label according to the corresponding image to be clustered and the assigned label.
  • the data transmission device acquires the image to be clustered of the mobile terminal, identifies the image to be clustered, extracts the image feature of the image to be clustered, uploads the image feature to the server for clustering, and uploads only the image feature to complete the image clustering. , without uploading the entire image, can reduce the amount of data transmitted and increase the transmission speed.
  • the image to be clustered can be assigned to the corresponding category according to the clustering result returned by the server, and the image is classified and displayed, which is easier to find and convenient.
  • the acquisition module 710 includes a comparison unit 712 and a determination unit 714.
  • the comparison unit 712 is configured to compare the image information stored in the first database and the second database, and generate a new image list and/or an updated image list according to the comparison result.
  • the determining unit 714 is configured to determine an image to be clustered according to the newly added image list and/or the updated image list.
  • the image to be clustered can be acquired, and only the images that need to be clustered can be clustered, which can reduce the pressure of the server and improve the efficiency of image clustering.
  • the matching unit 712 includes a lookup subunit, an add subunit, and a judging subunit.
  • the lookup subunit is configured to perform a lookup in the second database according to the path of the image in the first database.
  • the determining subunit is configured to determine whether the image in the first database is consistent with the modification time of the corresponding image in the second database if the corresponding image is found in the second database.
  • the determining subunit is further configured to determine whether the image in the first database is consistent with the message digest of the corresponding image in the second database if the modification time is inconsistent.
  • Add subunits also used to add inconsistent images to the updated image list if the message digests are inconsistent.
  • the first database and the second database may be compared, and a new image list and an updated image list may be generated to conveniently determine an image that needs to be clustered, so that only images that need to be clustered are clustered, which can be mitigated. Server stress and improve the efficiency of image clustering.
  • the uploading module 730 includes a grouping information extracting unit 732, a packing unit 734, and an uploading unit 736.
  • the grouping information extracting unit 732 is configured to extract current image grouping information and image features of the grouped images in the respective groups.
  • the packing unit 734 is configured to package the image grouping information, the image features of the grouped images in each group, and the image features of the image to be clustered into an uplink data packet.
  • the uploading unit 736 is configured to upload the uplink data packet to the server.
  • the uploading unit 736 is further configured to send a clustering request to the server, where the clustering request instructs the server to calculate the similarity between the image features of the image to be clustered and the image features of the grouped images in each group, and determine the image. Groups of features and assign corresponding tags.
  • the clustering request includes the mobile terminal identifier and the sending moment; when the clustering request is allocated to the server, the indication server obtains, according to the mobile terminal identifier, the clustering request that belongs to the same mobile terminal at different sending moments, and acquires The clustering request is merged.
  • the clustering request includes account information; when the clustering request is assigned to the server, the instructing server acquires clustering requests sent by the same account according to the account information, and merges the acquired clustering requests.
  • the clustering images may be grouped according to the existing grouping information and the image features of the grouped images in each group, so that the clustering result is more accurate, and the actual needs of the user are improved, and the image is improved.
  • the efficiency of clustering can increase the viscosity of the user.
  • the data transmission device 700 includes a power supply state acquisition module in addition to the acquisition module 710, the extraction module 720, the upload module 730, the receiving module 740, and the distribution module 750.
  • the power state obtaining module is configured to obtain a current power state. If the power state meets the preset state, the image to be clustered is acquired by the acquiring module 710.
  • the obtaining module 710 is further configured to acquire an image to be clustered of the mobile terminal if the current time is in a preset uploading period.
  • the state of the power source of the mobile terminal when uploading the image feature of the image to be clustered is ensured, and the feature of the uploaded image is reduced. The impact of the use of the terminal.
  • the embodiment of the present application further provides a mobile terminal.
  • a mobile terminal As shown in FIG. 10, for the convenience of description, only the parts related to the embodiments of the present application are shown. For the specific technical details not disclosed, refer to the method part of the embodiment of the present application.
  • the mobile terminal can be any mobile device, a tablet computer, a PDA (Personal Digital Assistant), a POS (Point of Sales), an on-board computer, a wearable device, or the like, and the mobile terminal is used as a mobile phone as an example. :
  • FIG. 10 is a block diagram showing a partial structure of a mobile phone related to a mobile terminal provided by an embodiment of the present application.
  • the mobile phone includes components such as a radio frequency (RF) circuit 1010, a memory 1020, an input unit 1030, a display unit 1040, a sensor 1050, an audio circuit 1060, a WiFi module 1070, a processor 1080, and a power source 1090.
  • RF radio frequency
  • the structure of the handset shown in FIG. 10 does not constitute a limitation to the handset, and may include more or less components than those illustrated, or some components may be combined, or different components may be arranged.
  • the RF circuit 1010 can be used for receiving and transmitting signals during the transmission and reception of information or during a call.
  • the downlink information of the base station can be received and processed by the processor 1080.
  • the uplink data can also be sent to the base station.
  • RF circuits include, but are not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like.
  • LNA Low Noise Amplifier
  • RF circuit 1010 can also communicate with the network and other devices via wireless communication.
  • the above wireless communication may use any communication standard or protocol, including but not limited to GSM, General Packet Radio Service (GPRS), CDMA, Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (Long Term Evolution, LTE)), e-mail, Short Messaging Service (SMS), etc.
  • GSM Global System for Mobile Communications
  • GPRS General Packet Radio Service
  • CDMA Code Division Multiple Access
  • WCDMA Wideband Code Division Multiple Access
  • LTE Long Term Evolution
  • SMS Short Messaging Service
  • the memory 1020 can be used to store software programs and modules, and the processor 1080 executes various functional applications and data processing of the mobile phone by running software programs and modules stored in the memory 1020.
  • the memory 1020 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as an application of a sound playing function, an application of an image playing function, etc.);
  • the data storage area can store data (such as audio data, address book, etc.) created according to the use of the mobile phone.
  • memory 1020 can include high speed random access memory, and can also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
  • the input unit 1030 can be configured to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the handset 1000.
  • the input unit 1030 may include a touch panel 1032 and other input devices 1034.
  • the touch panel 1032 which may also be referred to as a touch screen, can collect touch operations on or near the user (such as a user using a finger, a stylus, or the like on the touch panel 1032 or near the touch panel 1032. Operation) and drive the corresponding connection device according to a preset program.
  • the touch panel 1032 can include two portions of a touch detection device and a touch controller.
  • the touch detection device detects the touch orientation of the user, and detects a signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts the touch information into contact coordinates, and sends the touch information.
  • the processor 1080 is provided and can receive commands from the processor 1080 and execute them.
  • the touch panel 1032 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic waves.
  • the input unit 1030 can also include other input devices 1034.
  • other input devices 1034 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control buttons, switch buttons, etc.).
  • the display unit 1040 can be used to display information input by the user or information provided to the user as well as various menus of the mobile phone.
  • the display unit 1040 can include a display panel 1042.
  • the display panel 1042 may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.
  • the touch panel 1032 can cover the display panel 1042. When the touch panel 1032 detects a touch operation thereon or nearby, the touch panel 1032 transmits to the processor 1080 to determine the type of the touch event, and then the processor 1080 is The type of touch event provides a corresponding visual output on display panel 1042.
  • the touch panel 1032 and the display panel 1042 are used as two independent components to implement the input and input functions of the mobile phone, in some embodiments, the touch panel 1032 can be integrated with the display panel 1042. Realize the input and output functions of the phone.
  • the handset 1000 can also include at least one type of sensor 1050, such as a light sensor, motion sensor, and other sensors.
  • the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 1042 according to the brightness of the ambient light, and the proximity sensor may close the display panel 1042 and/or when the mobile phone moves to the ear. Or backlight.
  • the motion sensor may include an acceleration sensor, and the acceleration sensor can detect the magnitude of the acceleration in each direction, and the magnitude and direction of the gravity can be detected at rest, and can be used to identify the gesture of the mobile phone (such as horizontal and vertical screen switching), and vibration recognition related functions (such as Pedometer, tapping, etc.; in addition, the phone can also be equipped with gyroscopes, barometers, hygrometers, thermometers, infrared sensors and other sensors.
  • the acceleration sensor can detect the magnitude of the acceleration in each direction, and the magnitude and direction of the gravity can be detected at rest, and can be used to identify the gesture of the mobile phone (such as horizontal and vertical screen switching), and vibration recognition related functions (such as Pedometer, tapping, etc.; in addition, the phone can also be equipped with gyroscopes, barometers, hygrometers, thermometers, infrared sensors and other sensors.
  • Audio circuitry 1060, speaker 1062, and microphone 1064 can provide an audio interface between the user and the handset.
  • the audio circuit 1060 can transmit the converted electrical data of the received audio data to the speaker 1062, and convert it into a sound signal output by the speaker 1062.
  • the microphone 1064 converts the collected sound signal into an electrical signal, and the audio circuit 1060. After receiving, it is converted into audio data, and then processed by the audio data output processor 1080, transmitted to another mobile phone via the RF circuit 1010, or outputted to the memory 1020 for subsequent processing.
  • WiFi is a short-range wireless transmission technology.
  • the mobile phone through the WiFi module 1070 can help users to send and receive e-mail, browse the web and access streaming media, etc. It provides users with wireless broadband Internet access.
  • the processor 1080 is the control center of the handset, which connects various portions of the entire handset using various interfaces and lines, by executing or executing software programs and/or modules stored in the memory 1020, and invoking data stored in the memory 1020, The phone's various functions and processing data, so that the overall monitoring of the phone.
  • processor 1080 can include one or more processing units.
  • the processor 1080 can integrate an application processor and a modem processor, wherein the application processor primarily processes an operating system, a user interface, an application, and the like; the modem processor primarily processes wireless communications. It will be appreciated that the above described modem processor may also not be integrated into the processor 1080.
  • the mobile phone 1000 further includes a power source 1090 (such as a battery) for supplying power to various components.
  • a power source 1090 (such as a battery) for supplying power to various components.
  • the power source 1090 can be logically connected to the processor 1080 through a power management system to manage functions such as charging, discharging, and power management through a power management system. .
  • the handset 1000 may also include a camera, a Bluetooth module, and the like.
  • the processor 1080 included in the mobile terminal implements the above data transmission method when executing computer executable instructions stored in the memory.
  • a computer readable storage medium having stored thereon computer executable instructions that, when executed by a processor, implement the data transfer method described above.
  • the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or the like.
  • Non-volatile memory can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory can include random access memory (RAM), which acts as an external cache.
  • RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronization.
  • SRAM static RAM
  • DRAM dynamic RAM
  • SDRAM synchronous DRAM
  • DDR SDRAM dual data rate SDRAM
  • ESDRAM enhanced SDRAM
  • synchronization Link (Synchlink) DRAM (SLDRAM), Memory Bus (Rambus) Direct RAM (RDRAM), Direct Memory Bus Dynamic RAM (DRDRAM), and Memory Bus Dynamic RAM (RDRAM).

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Abstract

一种数据传输方法，包括：获取移动终端的待聚类图像；对所述待聚类图像进行识别，提取所述待聚类图像的图像特征；将所述图像特征上传至服务器；接收所述服务器返回的聚类结果，所述聚类结果包括与所述图像特征对应的待聚类图像标识，以及对所述图像特征进行聚类后所分配的标签；根据所述对应的待聚类图像标识及分配的标签，将所述待聚类图像分配至与所述标签对应的组别。

Description

数据传输方法、移动终端及计算机可读存储介质
相关申请的交叉引用
本申请要求于2017年09月15日提交中国专利局、申请号为2017108502669、发明名称为“数据传输方法、装置、移动终端及计算机可读存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及互联网技术领域，特别是涉及一种数据传输方法、移动终端及非易失性计算机可读存储介质。
背景技术
随着互联网技术的飞速发展，用户可在移动终端上存储大量的图片，可对移动终端上存储的大量的图片进行分类。在传统的方式中，当移动终端需要对存储的图片进行分类时，可将需要进行分类的图片全部上传至服务器，再由服务器进行分类，传输量大，传输速度慢。
发明内容
根据本申请的各种实施例提供一种数据传输方法、移动终端及非易失性计算机可读存储介质。
一种数据传输方法，应用于移动终端中，包括：
获取移动终端的待聚类图像；
对所述待聚类图像进行识别，提取所述待聚类图像的图像特征；
将所述图像特征上传至服务器；
接收所述服务器返回的聚类结果，所述聚类结果包括与所述图像特征对应的待聚类图像标识，以及对所述图像特征进行聚类后所分配的标签；及
根据所述对应的待聚类图像标识及分配的标签，将所述待聚类图像分配至与所述标签对应的组别。
一种移动终端，包括存储器及处理器，所述存储器中储存有计算机可执行指令，所述计算机可执行指令被所述处理器执行时，使得所述处理器执行以下操作:
获取移动终端的待聚类图像；
对所述待聚类图像进行识别，提取所述待聚类图像的图像特征；
将所述图像特征上传至服务器；
接收所述服务器返回的聚类结果，所述聚类结果包括与所述图像特征对应的待聚类图像标识，以及对所述图像特征进行聚类后所分配的标签；及
根据所述对应的待聚类图像标识及分配的标签，将所述待聚类图像分配至与所述标签对应的组别。
一个或多个包含计算机可执行指令的非易失性计算机可读存储介质，当所述计算机可执行指令被一个或多个处理器执行时，使得所述处理器执行以下操作：
获取移动终端的待聚类图像；
对所述待聚类图像进行识别，提取所述待聚类图像的图像特征；
将所述图像特征上传至服务器；
接收所述服务器返回的聚类结果，所述聚类结果包括与所述图像特征对应的待聚类图像标识，以及对所述图像特征进行聚类后所分配的标签；及
根据所述对应的待聚类图像标识及分配的标签，将所述待聚类图像分配至与所述标签 对应的组别。
通过获取移动终端的待聚类图像，对待聚类图像进行识别，提取待聚类图像的图像特征，将图像特征上传至服务器进行聚类，仅上传图像特征即可完成图像聚类，无需上传整张图像，可以减少传输的数据量，提高传输速度。此外，可根据服务器返回的聚类结果将待聚类图像分配到对应的类别中，将图像进行分类展示，更易于查找，方便快捷。
本申请的一个或多个实施例的细节在下面的附图和描述中提出。本申请的其它特征、目的和优点将从说明书、附图以及权利要求书变得明显。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为一个实施例中数据传输方法的应用场景图；
图2为一个实施例中移动终端与服务器进行交互的时序图；
图3为一个实施例中数据传输方法的流程图；
图4为一个实施例中获取待聚类图像的流程图；
图5为一个实施例中比对第一数据库及第二数据库中存储的图像的流程图；
图6为一个实施例中向服务器上传图像特征的流程图；
图7为一个实施例中数据传输装置的框图；
图8为一个实施例中获取模块的框图；
图9为一个实施例中上传模块的框图；
图10为一个实施例中移动终端的框图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本申请，并不用于限定本申请。
可以理解，本申请所使用的术语“第一”、“第二”等可在本文中用于描述各种元件，但这些元件不受这些术语限制。这些术语仅用于将第一个元件与另一个元件区分。举例来说，在不脱离本申请的范围的情况下，可以将第一客户端称为第二客户端，且类似地，可将第二客户端称为第一客户端。第一客户端和第二客户端两者都是客户端，但其不是同一客户端。
图1为一个实施例中数据传输方法的应用场景图。如图1所示，移动终端10可通过网络与服务器20建立通信连接，其中，服务器20可以是单独的一个服务器，也可以是由多个服务器组成的服务器集群，或是服务器集群中的某一台服务器。移动终端10获取待聚类图像，对待聚类图像进行识别，提取待聚类图像的图像特征。移动终端10将待聚类图像的图像特征上传至服务器20，服务器20根据移动终端10上传的待聚类图像的图像特征，对待聚类图像进行聚类，并向移动终端10返回聚类结果。移动终端10接收服务器20返回的聚类结果，聚类结果中可包括与图像特征对应的待聚类图像标识、以及对图像特征进行聚类后所分配的标签。移动终端10可根据该对应的待聚类图像标识及分配的标签，将待聚类图像分配至与标签对应的组别。
图2为一个实施例中移动终端与服务器进行交互的时序图。如图2所示，移动终端与服务器的主要交互过程可包括以下操作：
1.移动终端10对待聚类图像进行识别，提取待聚类图像的图像特征。
移动终端10可获取一张或多张待聚类图像，并对获取的各个待聚类图像进行识别，提取待聚类图像的图像特征。在一个实施例中，移动终端10可通过预设的人脸识别模型对待聚类图像进行分析，判断对应的待聚类图像中是否包含人脸。当检测到待聚类图像包含人脸时，可提取待聚类图像的人脸特征点，其中，人脸特征点可用于描述人脸形状、五官形状及位置。
2.移动终端10将图像特征上传至服务器20。
移动终端10可将提取的待聚类图像的图像特征上传至服务器20，图像特征可包括有对应的图像信息，其中，图像信息可包括与图像特征对应的待聚类图像标识，待聚类图像标识可以是待聚类图像的名称或编号等信息。
在一个实施例中，移动终端10可提取当前的图像分组信息及各个组别中已分组图像的图像特征，其中，图像分组信息可包括每个组的组别信息，以及每个组别下包含的图像信息等。移动终端可将图像分组信息、各个组别中已分组图像的图像特征及待聚类图像的图像特征打包成上行数据包，并将上行数据包发送至服务器20。
3.服务器20对待聚类图像进行聚类，并分配对应的标签。
移动终端10可向服务器20发送聚类请求，服务器20可根据移动终端10的聚类请求对待聚类图像的图像特征进行聚类。在一个实施例中，聚类请求可包括发送的对应移动终端10的标识、账户、发送时刻等信息，服务器20接收到聚类请求后可将聚类请求添加到队列服务中。服务器20进行聚类请求分配时，可将队列服务中属于同一移动终端在不同发送时刻发送的聚类请求进行合并，也可将队列服务中属于同一账户发送的聚类请求进行合并。
在一个实施例中，当服务器20接收到上行数据包及聚类请求后，可解析上行数据包，得到图像分组信息、各个组别中已分组图像的图像特征及待聚类图像的图像特征等信息。服务器20可通过预设的聚类模型，计算待聚类图像的图像特征与各个组别中已分组图像的图像特征的相似度，确定与图像特征对应的待聚类图像所属的组别，并分配对应的标签。
4.服务器20向移动终端10返回聚类结果。
5.移动终端10根据聚类结果中，与图像特征对应的待聚类图像标识及分配的标签，将待聚类图像分配至与标签对应的组别。
服务器20可将聚类结果返回给移动终端10，聚类结果中可包含与图像特征对应的待聚类图像标识及对图像特征进行聚类后所分配的标签。移动终端10可根据聚类结果将各个待聚类图像添加到对应的组别中，可从聚类结果中获取与图像特征对应的待聚类图像标识及分配的标签，将与图像特征对应的待聚类图像分配至与标签对应的组别中，并分配对应的组别标识。
如图3所示，在一个实施例中，提供一种数据传输方法，包括以下操作：
操作310，获取移动终端的待聚类图像。
具体地，移动终端可从内存等存储器中获取一张或多张待聚类图像。待聚类图像可以是用户在移动终端上拍摄的图像，也可以是从其他计算机设备上获取的图像。例如，待聚类图像可以是其他移动终端发送的图像，也可以是用户通过移动终端浏览网页时保存的图像等。在本实施例中，待聚类图像可以是照片，移动终端可通过服务器对待聚类图像进行聚类，从而生成相应的相册。
在一个实施例中，待聚类图像可以是移动终端上存储的没有分组的图像，也即，可以是没有被聚过类的图像，也可以是有对应的组别但是需要重新聚类的图像等。在本实施例中，待聚类图像可以是移动终端上存储的没有分组的图像，当图像进行聚类后，会分配有对应的组别标识，移动终端可获取存储的图像中没有对应组别标识的图像，作为待聚类图像。
在一个实施例中，当获取的待聚类图像有多张时，则移动终端可检测获取的多张待聚类图像中是否包含重复的待聚类图像，其中，重复的待聚类图像指的是相似度大于第一阈值的多张待聚类图像，当获取的多张待聚类图像中包含重复的待聚类图像时，则移动终端可从多张重复的待聚类图像中选取质量最高的待聚类图像进行识别，并提取该质量最高的待聚类图像的图像特征进行上传。移动终端可根据重复的各个待聚类图像中的饱和度、清晰度、亮度等值确定图像质量，并从中选取质量最高的待聚类图像进行识别。
操作320，对待聚类图像进行识别，提取待聚类图像的图像特征。
具体地，移动终端可对获取的各个待聚类图像进行识别，并提取待聚类图像的图像特征。在一个实施例中，服务器可根据人脸对图像进行聚类。移动终端可先对各个待聚类图像进行人脸识别，并将待聚类图像分为无人图像及人脸图像。进一步地，移动终端可通过预设的人脸识别模型对待聚类图像进行分析，判断对应的待聚类图像中是否包含人脸。在一个实施例中，人脸识别模型可以是预先通过机器学习构建的决策模型，构建人脸识别模型时，可获取大量的样本图像，样本图像中包含有人脸图像及无人图像，可根据每个样本图像是否包含人脸对样本图像进行标记，并将标记的样本图像作为人脸识别模型的输入，通过机器学习进行训练，得到人脸识别模型。
移动终端将待聚类图像分为无人图像及人脸图像后，可将无人图像分到对应的无人图像组别中，并添加对应的组别标识。在一个实施例中，移动终端可仅提取待聚类图像中人脸图像的图像特征，并根据人脸图像的图像特征进行聚类。移动终端可根据预设的特征模型提取各个人脸图像的图像特征，图像特征可包括形状特征、空间特征及边缘特征等，其中，形状特征指的是待聚类图像中局部的形状，空间特征指的是待聚类图像中分割出来的多个区域之间的相互的空间位置或相对方向关系，边缘特征指的是待聚类图像中组成两个区域之间的边界像素，但不限于此，还可包含颜色特征、纹理特征等。进一步地，移动终端对待聚类图像进行人脸识别，当检测到待聚类图像包含人脸时，可提取待聚类图像的人脸特征点，其中，人脸特征点可用于描述人脸形状、五官形状及位置。
操作330，将图像特征上传至服务器。
具体地，移动终端可将提取的待聚类图像的图像特征上传至服务器，图像特征可包括有对应的图像信息，其中，图像信息可包括与图像特征对应的待聚类图像标识，待聚类图像标识可以是待聚类图像的名称或编号等信息。服务器可根据各个待聚类图像的图像特征对待聚类图像进行聚类，将包含有相似图像特征的待聚类图像分为一类。在一个实施例中，服务器可根据人脸对待聚类图像进行聚类，服务器可根据移动终端上传的可用于描述待聚类图像中人脸形状及五官形状、位置等信息的特征点，将具有相似人脸特征点的待聚类图像分为一类。服务器可根据聚类结果为每个待聚类图像的图像特征添加对应的标签，标签可用于表示与图像特征对应的待聚类图像所属的组别。
操作340，接收服务器返回的聚类结果，聚类结果包括与图像特征对应的待聚类图像标识，以及对图像特征进行聚类后所分配的标签。
操作350，根据对应的待聚类图像标识及分配的标签，将待聚类图像分配至与标签对应的组别。
具体地，服务器可将聚类结果返回给移动终端。聚类结果中可包含与图像特征对应的待聚类图像标识及对图像特征进行聚类后所分配的标签。移动终端可根据聚类结果将各个待聚类图像添加到对应的组别中，可从聚类结果中获取与图像特征对应的待聚类图像标识及分配的标签，将与图像特征对应的待聚类图像分配至与标签对应的组别中，并分配对应的组别标识。在一个实施例中，移动终端可建立一个或多个相册，每个组别可分别对应一个相册，可将属于同一组别的图像在同一个相册中进行展示。
上述数据传输方法，获取移动终端的待聚类图像，对待聚类图像进行识别，提取待聚类图像的图像特征，将图像特征上传至服务器进行聚类，仅上传图像特征即可完成图像聚 类，无需上传整张图像，可以减少传输的数据量，提高传输速度。此外，可根据服务器返回的聚类结果将待聚类图像分配到对应的类别中，将图像进行分类展示，更易于查找，方便快捷。
如图4所示，在一个实施例中，操作310获取移动终端的待聚类图像，包括以下操作：
操作402，比对第一数据库及第二数据库中存储的图像信息，根据比对结果生成新增图像列表和更新图像列表中的至少一个。
具体地，在本实施例中，第一数据库指的是媒体数据库，媒体数据库可用于存储图像、视频、音频等多媒体文件的信息，可供视频播放器、音频播放器及相册图库使用。第一数据库中可包含有图像的存储路径、消息摘要、多媒体编号、修改时间等字段，用于存储图像的信息。在一个实施例中，第一数据库可包括SD卡(Secure Digital Memory Card)媒体数据库及内存媒体数据库，其中，SD卡媒体数据库可用于存储SD卡的多媒体信息，内存媒体数据库可用于存储内存中的多媒体信息。第二数据库指的是人脸数据库，人脸数据库中可存储有各个图像的人脸识别扫描结果、图像特征、组别信息等。人脸数据库中可包含有图片属性、人脸属性及组属性等多个类型的字段，其中，图片属性可包括图像的存储路径、消息摘要、多媒体编号、修改时间等字段，人脸属性可包括人脸状态、人脸大小、人脸特征等字段，组属性可包括组别标识、组名称、创建时间等字段，但不限于此。当移动终端采集一张新的图像时，例如可通过摄像头采集、或是从其他计算机设备接收等，移动终端需先将该图像存储在第一数据库中；当对该图像进行人脸识别扫描，提取图像特征，并根据图像特征进行聚类后，可将该图像的信息、以及对应的图像特征、组别信息等存储在人脸数据库中。
在其他的实施例中，除了根据人脸对图像进行聚类外，也可根据其他的特征进行聚类，例如场景、地点或时间等，则第二数据库可以是保存有用于聚类的特征信息及聚类结果等信息的数据库，并不仅限于上述的人脸数据库。
移动终端可将第一数据库存储的图像与第二数据库存储的图像信息进行比对，可根据存储路径、多媒体编号、修改时间或是消息摘要等字段进行比对，并生成新增图像列表和/或更新图像列表。在一个实施例中，新增图像列表可记录有移动终端中未进行人脸识别的图像，移动终端可将存在于第一数据库但不存在于第二数据库的图像添加到新增图像列表。更新图像列表可记录有在进行人脸识别后内容发生改变的图像，可将同时存在于第一数据库及第二数据库，但发生了改变的图像添加到更新图像列表等。移动终端可仅生成新增图像列表和更新图像列表中的一种，也可同时生成新增图像列表和更新图像列表。
操作404，根据新增图像列表和更新图像列表中的至少一个确定待聚类图像。
具体地，移动终端可直接将新增图像列表和/或更新图像列表中包含的图像作为待聚类图像，并提取待聚类图像的图像特征上传至服务器进行聚类。在一个实施例中，当移动终端生成更新图像列表时，移动终端可判断更新图像列表中是否存在有对应的分组但是需要重新聚类的图像，可提取更新图像列表中每个图像的图像特征，并将提取的图像特征与第二数据库中存储的对应的图像特征进行比较。当提取的图像特征与第二数据库中存储的对应的图像特征相似度大于或等于预设值时，则移动终端可判定该相似度大于预设值的图像可不重新进行聚类；当提取的图像特征与第二数据库中存储的对应的图像特征的相似度小于预设值时，则移动终端可判定该相似度小于预设值的图像需要重新进行聚类。移动终端可将新增图像列表，以及更新图像列表中需要重新进行聚类的图像作为待聚类图像。
在本实施例中，服务器可获取待聚类图像，可仅对需要进行聚类的图像进行聚类，可以减轻服务器的压力，并提高图像聚类的效率。
如图5所示，在一个实施例中，操作402比对第一数据库及第二数据库中存储的图像信息，根据比对结果生成新增图像列表和/或更新图像列表，包括以下操作：
操作502，根据第一数据库中图像的路径判断是否在第二数据库中查找到对应的图像， 当在第二数据库中查找到对应的图像时，则执行操作506，当在第二数据库中没有查找到对应的图像时，则执行操作504。
具体地，移动终端可根据第一数据库中图像的路径在第二数据库中查找，判断第二数据库中是否存储有该图像对应的人脸识别结果。移动终端可逐一读取第一数据库中存储的每个图像在存储路径字段的值，并查找第二数据库是否有存储路径字段的值与读取的值一致的图像，当有时，则第二数据库中存储路径字段的值与读取的值一致的图像，即为第二数据库中对应的图像。在一个实施例中，移动终端也可根据第一数据库中每个图像的多媒体编号在第二数据库中查找对应的图像，当在第二数据库中能查找到多媒体编号与第一数据库中一致的图像时，则该多媒体编号一致的图像即为第二数据库中对应的图像。
操作504，将没有查找到的图像添加到新增图像列表。
具体地，当移动终端根据第一数据库中图像的路径没有在第二数据库中查找到对应的图像时，则该图像只存在于第一数据库而不存在于第二数据库中，说明该图像未进行人脸识别扫描，可将第一数据库中没有在第二数据库中查找到对应图像的图像添加到新增图像列表。进一步地，新增图像列表中可记录有只存在于第一数据库而不存在于第二数据库的图像的标识信息，其中，标识信息可以是多媒体编号、存储路径等。
操作506，判断第一数据库中的图像与第二数据库中对应的图像的修改时间是否一致，当一致时，则执行操作512，当不一致时，则执行操作508。
具体地，当能在第二数据库中查找到对应的图像时，则提取第一数据库中图像的修改时间字段的值，以及第二数据库中对应的图像的修改时间字段的值，判断二者是否一致，当修改时间一致时，说明图像在进行人脸识别并存储在第二数据库后，没有进行过修改，则可不进行处理。
操作508，判断第一数据库中的图像与第二数据库中对应的图像的消息摘要是否一致，当一致时，则执行操作512，当不一致时，则执行操作510。
具体地，当第一数据库中的图像的修改时间，与第二数据库中对应的图像的修改时间不一致时，说明图像在进行人脸识别并存储在第二数据库后，进行过修改，则移动终端可进一步地提取该图像在第一数据库中存储的消息摘要字段的值，以及第二数据库中对应的图像的消息摘要字段的值，并比较是否一致。消息摘要也可称为数字摘要，每一个消息摘要是可唯一对应一个消息或文本等的固定长度的值，通过判断第一数据库中的图像与第二数据库中对应的图像的消息摘要是否一致，可判断该图像的内容是否发生了改变，当消息摘要不一致时，说明图像在进行人脸识别扫描并存储在第二数据库后，图像内容发生了变化，第一数据库中存储的图像与第二数据库中对应的图像不是同一内容的图像。
在一个实施例中，图像的消息摘要可以是图像的MD5(Message Digest Algorithm MD5， 消息摘要算法第五版)，也可以是其他的哈希算法等，并不限于此。移动终端每存储一张新的图像，或是对图像进行了修改等，即可根据MD5等算法计算图像的消息摘要，并将消息摘要与图像的多媒体编号、存储路径等信息关联存储在第一数据库中。
操作510，将消息摘要不一致的图像添加到更新图像列表。
具体地，移动终端可将第一数据库中，消息摘要与第二数据库中对应图像的消息摘要不同的图像添加到更新图像列表，更新图像列表中可记录有在进行人脸识别后内容发生了变化的图像，进一步地，可记录有在进行人脸识别扫描后内容发生了变化的图像的标识信息。
操作542，不作处理。
具体地，当图像在第一数据库的修改时间与第二数据库的修改时间不同，但是消息摘要相同时，说明该图像在进行人脸识别后发生了修改，但是没有改变图像内容，则移动终端可不进行处理。
在本实施例中，可对比第一数据库与第二数据库，生成新增图像列表和/或更新图像 列表，方便确定需要进行聚类的图像，从而仅对需要进行聚类的图像进行聚类，可以减轻服务器的压力，并提高图像聚类的效率。
如图6所示，在一个实施例中，操作330将图像特征上传至服务器，包括以下操作：
操作602，提取当前的图像分组信息及各个组别中已分组图像的图像特征。
具体地，移动终端可提取当前的图像分组信息，其中，图像分组信息可包括每个组的组别信息，例如组别标识、组名称、创建时间等信息，还可包括每个组别下包含的图像信息，例如包含的图像的标识信息、存储路径等。在一个实施例中，图像分组信息可用group_id：pic_id的形式表示，其中，group_id表示组别标识，pic_id表示图像的多媒体编号。进一步地，移动终端可从第二数据库中提取当前的图像分组信息，并将图像分组信息缓存到第三数据库中。第三数据库指的是备份数据库，可用于存储与服务器交互的信息，例如发送给服务器的信息，以及服务器下发的信息等。在第三数据库中，也可包含有图片属性、人脸属性及组属性等多个类型的字段，每个属性下的字段数量可比第二数据库少，仅保留与服务器交互时相关的字段，例如，图片属性可只包括存储路径、多媒体编号等字段，人脸属性可只包含有人脸特征等字段，组属性可只包括组别标识、创建时间等字段，但不限于此。
在一个实施例中，当前的图像分组信息可包括手动分组的分组信息及自动聚类的分组信息，其中，手动分组的分组信息指的是用户手动操作进行分组的分组信息，包括用户创建的分组、合并的分组，以及手动调整的照片所属的组别等，自动聚类的分组信息，指的是服务器或移动终端等根据各个图像的图像特征进行聚类生成的分组。
在一个实施例中，移动终端从第二数据库提取当前的图像分组信息，并将图像分组信息缓存在第三数据库后，可根据图像分组信息从第二数据库中提取各个组别中已分组图像的图像特征，可从第二数据库中提取每个组别下包含的各个图像的图像特征，并对应存储在第三数据库中。提取各个组别中已分组图像的图像特征，可确定各个组别对应的图像特征，例如，各个组别对应的人脸特征等，可帮助服务器对待聚类图像的图像特征进行聚类。
操作604，将图像分组信息、各个组别中已分组图像的图像特征及待聚类图像的图像特征打包成上行数据包。
具体地，移动终端可将图像分组信息、各个组别中已分组图像的图像特征及待聚类图像的图像特征按照预设格式打包成上行数据包，并将上行数据包上传至服务器进行图像聚类。在一个实施例中，移动终端可按照组别，将属于同一组别的图像的图像特征打包成同一上行数据包，并携带有对应组别的组别标识、组名称等组别信息等。
操作606，将上行数据包上传至服务器。
操作608，向服务器发送聚类请求，聚类请求指示服务器计算待聚类图像的图像特征与各个组别中已分组图像的图像特征的相似度，确定图像特征的组别并分配对应的标签。
具体地，移动终端可将上行数据包上传至服务器，并向服务器发送聚类请求。在一个实施例中，服务器可以是单一的服务器，也可以是由多个主机构成的分布式服务器集群，服务器集群中可包含有多个服务器，每个服务器均可向移动终端提供图像聚类服务。移动终端向服务器集群发送聚类请求后，服务器集群可将聚类请求添加到队列服务中，并根据队列服务为服务器集群中的各个服务器分配聚类请求，由分配到聚类请求的服务器进行图像聚类。队列服务中包含的每个聚类请求，均可携带有发送的对应移动终端的标识、账户、发送时刻等信息，其中，移动终端的标识可以是移动终端的MAC(Media Access Control，介质访问控制层)地址，或是IMSI(International Mobile Subscriber Identification Number，国际移动用户识别码)等。
在一个实施例中，服务器集群可按照队列服务中各聚类请求的发送时刻的先后顺序，为服务器集群的服务器分配聚类请求。服务器集群进行聚类请求分配时，可检测队列服务中是否包含有与分配的聚类请求属于同一移动终端在不同时刻发送的聚类请求，当包含 时，则可将与分配的聚类请求属于同一移动终端在不同发送时刻发送的聚类请求进行合并，并将合并后的聚类请求分配给服务器。例如，当前分配的聚类请求为移动终端A在2017年8月2号6：00发送的，检测到队列服务中还包含有移动终端A在2017年8月2号7：00发送的聚类请求以及移动终端A在2017年8月2号8：00发送的聚类请求，则服务器集群可将上述同属于移动终端A的上述三个聚类请求进行合并，并将合并后的聚类请求分配给服务器，由该服务器进行统一处理。进行聚类请求分配时，还可检测队列服务中是否包含有与分配的聚类请求属于同一账户在发送的聚类请求，当包含时，则可将与分配的聚类请求属于同一账户发送的聚类请求进行合并，并将合并后的聚类请求分配给服务器。例如，当前分配的聚类请求为账户X通过移动终端A在2017年8月2号6：00发送的，检测到队列服务中还包含有账户X通过移动终端B在2017年8月2号7：00发送的聚类请求，则服务器集群可将上述同属于账户X的两个聚类请求进行合并，并将合并后的聚类请求分配给服务器，由该服务器进行统一处理。
当服务器接收到上行数据包及聚类请求后，可解析上行数据包，得到图像分组信息、各个组别中已分组图像的图像特征及待聚类图像的图像特征等信息。服务器通过预设的聚类模型对各个待聚类图像进行聚类。进一步地，服务器可通过聚类模型，针对每个待聚类图像的图像特征，可分别计算与各个组别中已分组图像的图像特征的相似度。当待聚类图像的图像特征与组别中包含图像的图像特征的相似度大于第二阈值时，则可认为属于同一类图像，服务器可将该图像特征分配至相似度大于第二阈值的组别中，并分配与该组别匹配的标签，可建立该标签与对应的待聚类图像标识的对应关系。当不存在与待聚类图像的图像特征的相似度大于第二阈值的组别时，则说明该待聚类图像不属于已有的组别，可通过预设的聚类模型对不属于已有组别的待聚类图像的图像特征重新进行聚类，将具有相似图像特征的待聚类图像划分生成新的组别，并为属于同一新的组别的图像特征对应的待聚类图像分配对应的标签。在一个实施例中，当服务器接收到的是合并后的聚类请求时，则服务器直接获取与合并后的聚类请求对应的所有待聚类图像的图像特征，并进行聚类，可提高图像的聚类效率。
在一个实施例中，服务器可根据实际需求制定聚类策略，确定是仅对待聚类图像进行聚类，还是对移动终端历史上传了图像特征的所有图像进行聚类。比如，当服务器进行了聚类模型更新后，可制定对移动终端历史上传了图像特征的所有图像进行聚类的聚类策略，其中，对历史上传了图像特征的所有图像进行聚类时，可保留带有手动分组属性的组别，针对没有涉及用户手动操作的组别及图像，重新进行聚类。
当移动终端接收到服务器返回的聚类结果后，可根据与图像特征对应的待聚类图像及分配到的标签，将对应的待聚类图像添加到与标签匹配的组别中，并将聚类结果缓存到第三数据库中。移动终端可根据第三数据库中缓存的服务器返回的聚类结果，对第二数据库进行更新。
在本实施例中，可根据已有的分组信息以及各个组别中已分组图像的图像特征，对待聚类图像进行分组，可使聚类结果更加准确，并贴合用户的实际需求，提高图像聚类的效率的同时，可提高用户粘度。
在一个实施例中，在操作310获取移动终端的待聚类图像之前，可包括：获取当前的电源状态，当所述电源状态满足预设状态时，则执行操作310获取移动终端的待聚类图像。
具体地，移动终端在向服务器上传待聚类图像的图像特征之前，可先获取当前的电源状态，其中，电源状态可包括可用剩余电量、是否处于充电状态、用电速度等。当电源状态满足预设状态时，再获取待聚类图像，对待聚类图像进行识别，提取待聚类图像的图像特征，并将图像特征上传至服务器。预设状态可以是可用剩余电量大于预设百分比，或是处于充电状态，或是可用剩余电量大于预设百分比且用电速度小于设定值等，并不限于此，可根据实际需求进行设定。
在一个实施例中，移动终端可预先设定上传待聚类图像的图像特征的上传时间段，当当前的时刻处于预设的上传时间段时，则可向服务器上传待聚类图像的图像特征。上传的时间段可设定在较少使用移动终端的时间段，例如，凌晨的2点至4点等。
在本实施例中，当电源状态满足预设状态，再向服务器上传待聚类图像的图像特征，可保证上传待聚类图像的图像特征时移动终端的电源等状态，减少上传图像特征对移动终端的使用的影响。
在一个实施例中，提供一种数据传输方法，包括以下操作：
操作(1)，获取当前的电源状态。
操作(2)，当电源状态满足预设状态，则获取移动终端的待聚类图像。
操作(3)，对待聚类图像进行识别，提取待聚类图像的图像特征。
操作(4)，提取当前的图像分组信息及各个组别中已分组图像的图像特征，将图像分组信息、各个组别中已分组图像的图像特征及待聚类图像的图像特征打包成上行数据包。
操作(5)，将上行数据包上传至服务器。
操作(6)，向服务器发送聚类请求，聚类请求指示服务器计算待聚类图像的图像特征与各个组别中已分组图像的图像特征的相似度，确定各个待聚类图像的组别并分配对应的标签，其中，聚类请求包括移动终端标识及发送时刻，聚类请求被分配给所述服务器时，指示服务器根据所述移动终端标识获取属于同一移动终端在不同发送时刻发送的聚类请求，并对获取的聚类请求进行合并，聚类请求包括账户信息，聚类请求被分配给服务器时，指示服务器根据账户信息获取属于同一账户发送的聚类请求，并对获取的聚类请求进行合并。
操作(7)，接收服务器返回的聚类结果，聚类结果包括与图像特征对应的待聚类图像标识，以及对图像特征进行聚类后所分配的标签。
操作(8)，根据对应的待聚类图像标识及分配的标签，将待聚类图像分配至与标签对应的组别。
在本实施例中，获取待聚类图像，对待聚类图像进行识别，提取待聚类图像的图像特征，将图像特征上传至服务器进行聚类，仅上传图像特征即可完成图像聚类，无需上传整张图像，可以减少传输的数据量，提高传输速度。
本申请实施例的方法流程图中的各个操作按照箭头的指示依次显示，但是这些操作并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些操作的执行并没有严格的顺序限制，其可以以其他的顺序执行。而且，本申请实施例的方法流程图中的至少一部分操作可以包括多个子操作或者多个阶段，这些子操作或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，其执行顺序也不必然是依次进行，而是可以与其他操作或者其他操作的子操作或者阶段的至少一部分轮流或者交替地执行。
如图7所示，在一个实施例中，提供一种数据传输装置700，包括获取模块710、提取模块720、上传模块730、接收模块740及分配模块750。
获取模块710，用于获取移动终端的待聚类图像。
提取模块720，用于对待聚类图像进行识别，提取待聚类图像的图像特征。
在一个实施例中，提取模块720，还用于对待聚类图像进行人脸识别，当检测到待聚类图像包含人脸时，提取待聚类图像的人脸特征点，人脸特征点用于描述人脸形状、五官形状及位置。
上传模块730，用于将图像特征上传至服务器。
接收模块740，用于接收服务器返回的聚类结果，聚类结果包括与图像特征对应的待聚类图像标识，以及对图像特征进行聚类后所分配的标签。
分配模块750，用于根据对应的待聚类图像标识及分配的标签，将待聚类图像分配至与标签对应的组别。
上述数据传输装置，获取移动终端的待聚类图像，对待聚类图像进行识别，提取待聚类图像的图像特征，将图像特征上传至服务器进行聚类，仅上传图像特征即可完成图像聚类，无需上传整张图像，可以减少传输的数据量，提高传输速度。此外，可根据服务器返回的聚类结果将待聚类图像分配到对应的类别中，将图像进行分类展示，更易于查找，方便快捷。
如图8所示，在一个实施例中，获取模块710，包括比对单元712及确定单元714。
比对单元712，用于比对第一数据库及第二数据库中存储的图像信息，根据比对结果生成新增图像列表和/或更新图像列表。
确定单元714，用于根据新增图像列表和/或更新图像列表确定待聚类图像。
在本实施例中，可获取待聚类图像，可仅对需要进行聚类的图像进行聚类，可以减轻服务器的压力，并提高图像聚类的效率。
在一个实施例中，比对单元712，包括查找子单元、添加子单元及判断子单元。
查找子单元，用于根据第一数据库中图像的路径在第二数据库中进行查找。
添加子单元，用于若在第二数据库中没有查找到对应的图像，则将没有查找到的图像添加到新增图像列表。
判断子单元，用于若在第二数据库中查找到对应的图像，则判断第一数据库中的图像与第二数据库中对应的图像的修改时间是否一致。
判断子单元，还用于若修改时间不一致，则判断第一数据库中的图像与第二数据库中对应的图像的消息摘要是否一致。
添加子单元，还用于若消息摘要不一致，则将不一致的图像添加到更新图像列表。
在本实施例中，可对比第一数据库与第二数据库，生成新增图像列表及更新图像列表，方便确定需要进行聚类的图像，从而仅对需要进行聚类的图像进行聚类，可以减轻服务器的压力，并提高图像聚类的效率。
如图9所示，在一个实施例中，上传模块730，包括分组信息提取单元732、打包单元734及上传单元736。
分组信息提取单元732，用于提取当前的图像分组信息及各个组别中已分组图像的图像特征。
打包单元734，用于将图像分组信息、各个组别中已分组图像的图像特征及待聚类图像的图像特征打包成上行数据包。
上传单元736，用于将上行数据包上传至服务器。
在一个实施例中，上传单元736，还用于向服务器发送聚类请求，聚类请求指示服务器计算待聚类图像的图像特征与各个组别中已分组图像的图像特征的相似度，确定图像特征的组别并分配对应的标签。
在一个实施例中，聚类请求包括移动终端标识及发送时刻；聚类请求被分配给服务器时，指示服务器根据移动终端标识获取属于同一移动终端在不同发送时刻发送的聚类请求，并对获取的聚类请求进行合并。
在一个实施例中，聚类请求包括账户信息；聚类请求被分配给服务器时，指示服务器根据所述账户信息获取属于同一账户发送的聚类请求，并对获取的聚类请求进行合并。
在本实施例中，可根据已有的分组信息以及各个组别中已分组图像的图像特征，对待聚类图像进行分组，可使聚类结果更加准确，并贴合用户的实际需求，提高图像聚类的效率的同时，可提高用户粘度。
在一个实施例中，上述数据传输装置700，除了包括获取模块710、提取模块720、上传模块730、接收模块740及分配模块750，还包括电源状态获取模块。
电源状态获取模块，用于获取当前的电源状态，若电源状态满足预设状态，则通过获取模块710获取待聚类图像。
在一个实施例中，获取模块710，还用于若当前时刻处于预设的上传时间段，则获取移动终端的待聚类图像。
在本实施例中，当电源状态满足预设状态，再向服务器上传待聚类图像的图像特征，可保证上传待聚类图像的图像特征时移动终端的电源等状态，减少上传图像特征对移动终端的使用的影响。
本申请实施例还提供了一种移动终端。如图10所示，为了便于说明，仅示出了与本申请实施例相关的部分，具体技术细节未揭示的，请参照本申请实施例方法部分。该移动终端可以为包括手机、平板电脑、PDA(Personal Digital Assistant，个人数字助理)、POS(Point of Sales，销售终端)、车载电脑、穿戴式设备等任意终端设备，以移动终端为手机为例：
图10为与本申请实施例提供的移动终端相关的手机的部分结构的框图。参考图10，手机包括：射频(Radio Frequency，RF)电路1010、存储器1020、输入单元1030、显示单元1040、传感器1050、音频电路1060、WiFi模块1070、处理器1080、以及电源1090等部件。本领域技术人员可以理解，图10所示的手机结构并不构成对手机的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。
其中，RF电路1010可用于收发信息或通话过程中，信号的接收和发送，可将基站的下行信息接收后，给处理器1080处理；也可以将上行的数据发送给基站。通常，RF电路包括但不限于天线、至少一个放大器、收发信机、耦合器、低噪声放大器(Low Noise Amplifier，LNA)、双工器等。此外，RF电路1010还可以通过无线通信与网络和其他设备通信。上述无线通信可以使用任一通信标准或协议，包括但不限于GSM、通用分组无线服务(General Packet Radio Service，GPRS)、CDMA、宽带码分多址(Wideband Code Division Multiple Access，WCDMA)、长期演进(Long Term Evolution，LTE))、电子邮件、短消息服务(Short Messaging Service，SMS)等。
存储器1020可用于存储软件程序以及模块，处理器1080通过运行存储在存储器1020的软件程序以及模块，从而执行手机的各种功能应用以及数据处理。存储器1020可主要包括程序存储区和数据存储区，其中，程序存储区可存储操作系统、至少一个功能所需的应用程序(比如声音播放功能的应用程序、图像播放功能的应用程序等)等；数据存储区可存储根据手机的使用所创建的数据(比如音频数据、通讯录等)等。此外，存储器1020可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。
输入单元1030可用于接收输入的数字或字符信息，以及产生与手机1000的用户设置以及功能控制有关的键信号输入。具体地，输入单元1030可包括触控面板1032以及其他输入设备1034。触控面板1032，也可称为触摸屏，可收集用户在其上或附近的触摸操作(比如用户使用手指、触笔等任何适合的物体或附件在触控面板1032上或在触控面板1032附近的操作)，并根据预先设定的程式驱动相应的连接装置。在一个实施例中，触控面板1032可包括触摸检测装置和触摸控制器两个部分。其中，触摸检测装置检测用户的触摸方位，并检测触摸操作带来的信号，将信号传送给触摸控制器；触摸控制器从触摸检测装置上接收触摸信息，并将它转换成触点坐标，再送给处理器1080，并能接收处理器1080发来的命令并加以执行。此外，可以采用电阻式、电容式、红外线以及表面声波等多种类型实现触控面板1032。除了触控面板1032，输入单元1030还可以包括其他输入设备1034。具体地，其他输入设备1034可以包括但不限于物理键盘、功能键(比如音量控制按键、开关按键等)等中的一种或多种。
显示单元1040可用于显示由用户输入的信息或提供给用户的信息以及手机的各种菜单。显示单元1040可包括显示面板1042。在一个实施例中，可以采用液晶显示 器(Liquid Crystal Display，LCD)、有机发光二极管(Organic Light-Emitting Diode,OLED)等形式来配置显示面板1042。在一个实施例中，触控面板1032可覆盖显示面板1042，当触控面板1032检测到在其上或附近的触摸操作后，传送给处理器1080以确定触摸事件的类型，随后处理器1080根据触摸事件的类型在显示面板1042上提供相应的视觉输出。虽然在图10中，触控面板1032与显示面板1042是作为两个独立的部件来实现手机的输入和输入功能，但是在某些实施例中，可以将触控面板1032与显示面板1042集成而实现手机的输入和输出功能。
手机1000还可包括至少一种传感器1050，比如光传感器、运动传感器以及其他传感器。具体地，光传感器可包括环境光传感器及接近传感器，其中，环境光传感器可根据环境光线的明暗来调节显示面板1042的亮度，接近传感器可在手机移动到耳边时，关闭显示面板1042和/或背光。运动传感器可包括加速度传感器，通过加速度传感器可检测各个方向上加速度的大小，静止时可检测出重力的大小及方向，可用于识别手机姿态的应用(比如横竖屏切换)、振动识别相关功能(比如计步器、敲击)等；此外，手机还可配置陀螺仪、气压计、湿度计、温度计、红外线传感器等其他传感器等。
音频电路1060、扬声器1062和传声器1064可提供用户与手机之间的音频接口。音频电路1060可将接收到的音频数据转换后的电信号，传输到扬声器1062，由扬声器1062转换为声音信号输出；另一方面，传声器1064将收集的声音信号转换为电信号，由音频电路1060接收后转换为音频数据，再将音频数据输出处理器1080处理后，经RF电路1010可以发送给另一手机，或者将音频数据输出至存储器1020以便后续处理。
WiFi属于短距离无线传输技术，手机通过WiFi模块1070可以帮助用户收发电子邮件、浏览网页和访问流式媒体等，它为用户提供了无线的宽带互联网访问。
处理器1080是手机的控制中心，利用各种接口和线路连接整个手机的各个部分，通过运行或执行存储在存储器1020内的软件程序和/或模块，以及调用存储在存储器1020内的数据，执行手机的各种功能和处理数据，从而对手机进行整体监控。在一个实施例中，处理器1080可包括一个或多个处理单元。在一个实施例中，处理器1080可集成应用处理器和调制解调处理器，其中，应用处理器主要处理操作系统、用户界面和应用程序等；调制解调处理器主要处理无线通信。可以理解的是，上述调制解调处理器也可以不集成到处理器1080中。
手机1000还包括给各个部件供电的电源1090(比如电池)，优选的，电源1090可以通过电源管理系统与处理器1080逻辑相连，从而通过电源管理系统实现管理充电、放电、以及功耗管理等功能。
在一个实施例中，手机1000还可以包括摄像头、蓝牙模块等。
在本申请实施例中，该移动终端所包括的处理器1080执行存储在存储器上的计算机可执行指令时实现上述的数据传输方法。
在一个实施例中，提供一种计算机可读存储介质，其上存储有计算机可执行指令，该计算机可执行指令被处理器执行时实现上述的数据传输方法。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机可执行指令来指令相关的硬件来完成，所述的程序可存储于一非易失性计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，所述的存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory，ROM)等。
如此处所使用的对存储器、存储、数据库或其它介质的任何引用可包括非易失性和/或易失性存储器。合适的非易失性存储器可包括只读存储器(ROM)、可编程ROM (PROM)、电可编程ROM(EPROM)、电可擦除可编程ROM(EEPROM)或闪存。易失性存储器可包括随机存取存储器(RAM)，它用作外部高速缓冲存储器。作为说明而非局限，RAM以多种形式可得，诸如静态RAM(SRAM)、动态RAM(DRAM)、同步DRAM(SDRAM)、双数据率SDRAM(DDR SDRAM)、增强型SDRAM(ESDRAM)、同步链路(Synchlink)DRAM(SLDRAM)、存储器总线(Rambus)直接RAM(RDRAM)、直接存储器总线动态RAM(DRDRAM)、以及存储器总线动态RAM(RDRAM)。
以上所述实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上所述实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请专利的保护范围应以所附权利要求为准。

Claims (30)

  1. 一种数据传输方法，应用于移动终端中，包括：
    获取移动终端的待聚类图像；
    对所述待聚类图像进行识别，提取所述待聚类图像的图像特征；
    将所述图像特征上传至服务器；
    接收所述服务器返回的聚类结果，所述聚类结果包括与所述图像特征对应的待聚类图像标识，以及对所述图像特征进行聚类后所分配的标签；及
    根据所述对应的待聚类图像标识及分配的标签，将所述待聚类图像分配至与所述标签对应的组别。
  2. 根据权利要求1所述的方法，其特征在于，所述获取移动终端的待聚类图像，包括：
    比对第一数据库及第二数据库中存储的图像信息，根据比对结果生成新增图像列表和更新图像列表中的至少一个；
    根据所述新增图像列表和所述更新图像列表中的至少一个确定所述待聚类图像。
  3. 根据权利要求1所述的方法，其特征在于，所述对所述待聚类图像进行识别，包括：
    当获取的待聚类图像有多张时，检测多张所述待聚类图像中是否包含重复图像；
    当所述多张待聚类图像中包含重复的待聚类图像时，对所述重复的待聚类图像中质量最高的所述待聚类图像进行识别。
  4. 根据权利要求1所述的方法，其特征在于，所述将所述图像特征上传至服务器，包括：
    提取当前的图像分组信息及各个组别中已分组图像的图像特征；
    将所述图像分组信息、各个组别中已分组图像的图像特征及待聚类图像的图像特征打包成上行数据包；及
    将所述上行数据包上传至服务器。
  5. 根据权利要求4所述的方法，其特征在于，在所述将所述上行数据包上传至服务器之后，还包括：
    向所述服务器发送聚类请求，所述聚类请求指示所述服务器计算所述待聚类图像的图像特征与各个组别中已分组图像的图像特征的相似度，确定所述图像特征的组别并分配对应的标签。
  6. 根据权利要求5所述的方法，其特征在于，所述聚类请求包括移动终端标识及发送时刻；
    所述聚类请求被分配给所述服务器时，指示所述服务器根据所述移动终端标识获取属于同一移动终端在不同发送时刻发送的聚类请求，并对获取的聚类请求进行合并。
  7. 根据权利要求5所述的方法，其特征在于，所述聚类请求包括账户信息；
    所述聚类请求被分配给所述服务器时，指示所述服务器根据所述账户信息获取属于同一账户发送的聚类请求，并对获取的聚类请求进行合并。
  8. 根据权利要求1所述的方法，其特征在于，所述对所述待聚类图像进行识别，提取所述待聚类图像的图像特征，包括：
    对所述待聚类图像进行人脸识别，当检测到所述待聚类图像包含人脸时，提取所述待聚类图像的人脸特征点，所述人脸特征点用于描述人脸形状、五官形状及位置。
  9. 根据权利要求1至8任一所述的方法，其特征在于，在所述获取移动终端的待聚类图像之前，还包括：
    获取当前的电源状态，当所述电源状态满足预设状态时，则执行所述获取移动终端的待聚类图像。
  10. 根据权利要求1至8任一所述的方法，其特征在于，在所述获取移动终端的待聚类图像之前，还包括：
    当当前时刻处于预设的上传时间段时，则执行所述获取移动终端的待聚类图像。
  11. 一种移动终端，包括存储器及处理器，所述存储器中储存有计算机可执行指令，所述计算机可执行指令被所述处理器执行时，使得所述处理器执行如下操作：
    获取移动终端的待聚类图像；
    对所述待聚类图像进行识别，提取所述待聚类图像的图像特征；
    将所述图像特征上传至服务器；
    接收所述服务器返回的聚类结果，所述聚类结果包括与所述图像特征对应的待聚类图像标识，以及对所述图像特征进行聚类后所分配的标签；及
    根据所述对应的待聚类图像标识及分配的标签，将所述待聚类图像分配至与所述标签对应的组别。
  12. 根据权利要求11所述的移动终端，其特征在于，所述获取移动终端的待聚类图像，包括：
    比对第一数据库及第二数据库中存储的图像信息，根据比对结果生成新增图像列表和更新图像列表中的至少一个；
    根据所述新增图像列表和所述更新图像列表中的至少一个确定所述待聚类图像。
  13. 根据权利要求11所述的移动终端，其特征在于，所述对所述待聚类图像进行识别，包括：
    当获取的待聚类图像有多张时，检测多张所述待聚类图像中是否包含重复图像；
    当所述多张待聚类图像中包含重复的待聚类图像时，对所述重复的待聚类图像中质量最高的所述待聚类图像进行识别。
  14. 根据权利要求11所述的移动终端，其特征在于，所述将所述图像特征上传至服务器，包括：
    提取当前的图像分组信息及各个组别中已分组图像的图像特征；
    将所述图像分组信息、各个组别中已分组图像的图像特征及待聚类图像的图像特征打包成上行数据包；及
    将所述上行数据包上传至服务器。
  15. 根据权利要求14所述的移动终端，其特征在于，在所述将所述上行数据包上传至服务器之后，还包括：
    向所述服务器发送聚类请求，所述聚类请求指示所述服务器计算所述待聚类图像的图像特征与各个组别中已分组图像的图像特征的相似度，确定所述图像特征的组别并分配对应的标签。
  16. 根据权利要求15所述的移动终端，其特征在于，所述聚类请求包括移动终端标识及发送时刻；
    所述聚类请求被分配给所述服务器时，指示所述服务器根据所述移动终端标识获取属于同一移动终端在不同发送时刻发送的聚类请求，并对获取的聚类请求进行合并。
  17. 根据权利要求15所述的移动终端，其特征在于，所述聚类请求包括账户信息；
    所述聚类请求被分配给所述服务器时，指示所述服务器根据所述账户信息获取属于同一账户发送的聚类请求，并对获取的聚类请求进行合并。
  18. 根据权利要求11所述的移动终端，其特征在于，所述对所述待聚类图像进行识别，提取所述待聚类图像的图像特征，包括：
    对所述待聚类图像进行人脸识别，当检测到所述待聚类图像包含人脸时，提取所述待聚类图像的人脸特征点，所述人脸特征点用于描述人脸形状、五官形状及位置。
  19. 根据权利要求11至18任一所述的移动终端，其特征在于，在所述获取移动终端的待聚类图像之前，还包括：
    获取当前的电源状态，当所述电源状态满足预设状态时，则执行所述获取移动终端的待聚类图像。
  20. 根据权利要求11至18任一所述的移动终端，其特征在于，在所述获取移动终端的待聚类图像之前，还包括：
    当当前时刻处于预设的上传时间段时，则执行所述获取移动终端的待聚类图像。
  21. 一个或多个包含计算机可执行指令的非易失性计算机可读存储介质，当所述计算机可执行指令被一个或多个处理器执行时，使得所述处理器执行以下操作：
    获取移动终端的待聚类图像；
    对所述待聚类图像进行识别，提取所述待聚类图像的图像特征；
    将所述图像特征上传至服务器；
    接收所述服务器返回的聚类结果，所述聚类结果包括与所述图像特征对应的待聚类图像标识，以及对所述图像特征进行聚类后所分配的标签；及
    根据所述对应的待聚类图像标识及分配的标签，将所述待聚类图像分配至与所述标签对应的组别。
  22. 根据权利要求21所述的非易失性计算机可读存储介质，其特征在于，所述获取移动终端的待聚类图像，包括：
    比对第一数据库及第二数据库中存储的图像信息，根据比对结果生成新增图像列表和更新图像列表中的至少一个；
    根据所述新增图像列表和所述更新图像列表中的至少一个确定所述待聚类图像。
  23. 根据权利要求21所述的非易失性计算机可读存储介质，其特征在于，所述对所述待聚类图像进行识别，包括：
    当获取的待聚类图像有多张时，检测多张所述待聚类图像中是否包含重复图像；
    当所述多张待聚类图像中包含重复的待聚类图像时，对所述重复的待聚类图像中质量最高的所述待聚类图像进行识别。
  24. 根据权利要求21所述的非易失性计算机可读存储介质，其特征在于，所述将所述图像特征上传至服务器，包括：
    提取当前的图像分组信息及各个组别中已分组图像的图像特征；
    将所述图像分组信息、各个组别中已分组图像的图像特征及待聚类图像的图像特征打包成上行数据包；及
    将所述上行数据包上传至服务器。
  25. 根据权利要求24所述的非易失性计算机可读存储介质，其特征在于，在所述将所述上行数据包上传至服务器之后，还包括：
    向所述服务器发送聚类请求，所述聚类请求指示所述服务器计算所述待聚类图像的图像特征与各个组别中已分组图像的图像特征的相似度，确定所述图像特征的组别并分配对应的标签。
  26. 根据权利要求25所述的非易失性计算机可读存储介质，其特征在于，所述聚类请求包括移动终端标识及发送时刻；
    所述聚类请求被分配给所述服务器时，指示所述服务器根据所述移动终端标识获取属于同一移动终端在不同发送时刻发送的聚类请求，并对获取的聚类请求进行合并。
  27. 根据权利要求25所述的非易失性计算机可读存储介质，其特征在于，所述聚类请求包括账户信息；
    所述聚类请求被分配给所述服务器时，指示所述服务器根据所述账户信息获取属于同一账户发送的聚类请求，并对获取的聚类请求进行合并。
  28. 根据权利要求21所述的非易失性计算机可读存储介质，其特征在于，所述对所述待聚类图像进行识别，提取所述待聚类图像的图像特征，包括：
    对所述待聚类图像进行人脸识别，当检测到所述待聚类图像包含人脸时，提取所述待聚类图像的人脸特征点，所述人脸特征点用于描述人脸形状、五官形状及位置。
  29. 根据权利要求21至28任一所述的非易失性计算机可读存储介质，其特征在于，在所述获取移动终端的待聚类图像之前，还包括：
    获取当前的电源状态，当所述电源状态满足预设状态时，则执行所述获取移动终端的待聚类图像。
  30. 根据权利要求21至28任一所述的非易失性计算机可读存储介质，其特征在于，在所述获取移动终端的待聚类图像之前，还包括：
    当当前时刻处于预设的上传时间段时，则执行所述获取移动终端的待聚类图像。
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