WO2020186777A1 - Procédé, appareil et dispositif de récupération d'image et support de stockage lisible par ordinateur - Google Patents

Procédé, appareil et dispositif de récupération d'image et support de stockage lisible par ordinateur Download PDF

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Publication number
WO2020186777A1
WO2020186777A1 PCT/CN2019/117191 CN2019117191W WO2020186777A1 WO 2020186777 A1 WO2020186777 A1 WO 2020186777A1 CN 2019117191 W CN2019117191 W CN 2019117191W WO 2020186777 A1 WO2020186777 A1 WO 2020186777A1
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image
subject
feature
preset
retrieved
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PCT/CN2019/117191
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English (en)
Chinese (zh)
Inventor
陈世喆
王威
吴力丰
王昊
何维
谢树铭
龚阳
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平安科技（深圳）有限公司
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    • 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
    • G06F16/53—Querying
    • 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
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content

Definitions

  • This application relates to the field of image retrieval technology, and in particular to an image retrieval method, device, equipment and computer-readable storage medium.
  • the main purpose of this application is to provide an image retrieval method, device, equipment, and computer-readable storage medium, aiming to solve the technical problem of low image retrieval accuracy in the existing image retrieval technology.
  • this application provides an image retrieval method, the image retrieval method includes:
  • the candidate images are accurately sorted based on a preset sorting algorithm, and the candidate images are output in the sorted order.
  • an image retrieval device which includes:
  • the feature extraction module is used to obtain the image to be retrieved, and perform subject feature extraction and non-subject feature extraction on the image to be retrieved, so as to determine the image label corresponding to the image to be retrieved based on the extracted subject feature and non-subject feature;
  • the tag retrieval module is configured to perform retrieval in a preset image database based on the image tag, and determine the image set corresponding to the image tag;
  • the feature matching module is used to determine the image feature of the image to be retrieved based on a preset image feature extraction algorithm, and perform feature matching in the image set according to the image feature to determine the candidate corresponding to the image to be retrieved image;
  • the image sorting module is configured to accurately sort the candidate images based on a preset sorting algorithm, and output the candidate images in the sorted order.
  • the present application also provides an image retrieval device, the image retrieval device includes: a memory, a processor, and computer-readable instructions stored on the memory and running on the processor, When the computer-readable instructions are executed by the processor, the steps of the image retrieval method described above are implemented.
  • the computer-readable storage medium may be a non-volatile readable storage medium on which computer-readable instructions are stored.
  • the steps of the image retrieval method described above are realized.
  • This application belongs to the field of image retrieval technology, and proposes an image retrieval method.
  • the image to be retrieved is acquired, and the corresponding image tag is determined based on the subject and non-subject features of the image to be retrieved.
  • the image tag is used in the preset Search in the image database, determine the image set corresponding to the image tag, and further determine the image feature of the image to be retrieved based on the preset image feature extraction algorithm, so as to perform feature matching in the determined image set according to the image feature to determine the image feature to be retrieved.
  • the candidate images corresponding to the retrieved images are finally sorted with accuracy through a preset sorting algorithm, and the final sorted candidate images are output as the retrieval result.
  • the image retrieval method proposed in this application determines image tags by extracting subject features and non-subject features to perform preliminary retrieval to narrow the retrieval range, and then accurately obtain retrieval results that match the image to be retrieved through image feature matching. Finally, The search results are reordered through the sorting algorithm to provide users with more accurate search results and improve the efficiency and accuracy of image search.
  • FIG. 1 is a schematic diagram of the hardware structure of an image retrieval device involved in a solution of an embodiment of the application
  • FIG. 3 is a detailed flowchart of step S20 in FIG. 2;
  • FIG. 4 is a schematic flowchart of a second embodiment of an image retrieval method according to this application.
  • FIG. 5 is a schematic diagram of the functional modules of the first embodiment of the image retrieval device of this application.
  • the main solution of the embodiment of the application is to obtain the image to be retrieved, and perform subject feature extraction and non-subject feature extraction on the image to be retrieved, so as to determine the corresponding image to be retrieved based on the extracted subject feature and non-subject feature Image tags; search based on the image tags in a preset image database to determine the image set corresponding to the image tags; determine the image features of the image to be retrieved based on the preset image feature extraction algorithm, and according to the Image features perform feature matching in the image set to determine the candidate image corresponding to the image to be retrieved; perform accurate sorting on the candidate images based on a preset sorting algorithm, and output the sorted images in the sorted order Alternative image.
  • FIG. 1 is a schematic diagram of the hardware structure of the image retrieval device involved in the solution of the embodiment of the application.
  • the image retrieval method involved in the embodiments of the present application is mainly applied to an image retrieval device, and the image retrieval device may be a device with display and processing functions such as a PC, a portable computer, and a mobile terminal.
  • the image retrieval device may include a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005.
  • the communication bus 1002 is used to implement connection and communication between these components.
  • the user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
  • the network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).
  • the memory 1005 can be a high-speed RAM memory or a stable memory (non-volatile memory), such as disk storage.
  • the memory 1005 may also be a storage device independent of the foregoing processor 1001.
  • the image retrieval device may also include a camera, RF (Radio Frequency, radio frequency) circuits, sensors, audio circuits, Wi-Fi modules, etc.
  • sensors such as light sensors, motion sensors and other sensors.
  • the image retrieval device can also be equipped with other sensors such as gyroscope, barometer, hygrometer, thermometer, infrared sensor, etc., which will not be described here.
  • FIG. 1 does not constitute a limitation on the image retrieval device, and may include more or less components than shown in the figure, or a combination of certain components, or different components Layout.
  • a memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and computer readable instructions.
  • the network interface 1004 is mainly used to connect to the back-end server and communicate with the back-end server;
  • the user interface 1003 is mainly used to connect to the client (user side) and communicate with the client;
  • the processor 1001, the memory 1005 It may be set in an image retrieval device, which calls computer-readable instructions stored in the memory 1005 through the processor 1001, and executes the image retrieval method provided in the embodiment of the present application.
  • the solution provided in this embodiment first obtains the image to be retrieved, and extracts and determines the corresponding image label based on the subject feature and non-subject feature of the image to be retrieved, and uses the image tag to search in a preset image database to determine the image label Corresponding image set, and further determine the image feature of the image to be retrieved based on the preset image feature extraction algorithm, so as to perform feature matching in the determined image set according to the image feature to determine the candidate image corresponding to the image to be retrieved, Finally, the candidate images are accurately sorted by a preset sorting algorithm, and the final sorted candidate images are output as the retrieval result.
  • the image retrieval method proposed in this application determines image tags by extracting subject features and non-subject features to perform preliminary retrieval to narrow the retrieval range, and then accurately obtain retrieval results that match the image to be retrieved through image feature matching. Finally, The search results are reordered through the sorting algorithm to provide users with more accurate search results and improve the efficiency and accuracy of image search.
  • Fig. 2 is a schematic flowchart of a first embodiment of an image retrieval method according to this application.
  • the method includes:
  • Step S10 Obtain the image to be retrieved, and perform subject feature extraction and non-subject feature extraction on the image to be retrieved, so as to determine the image label corresponding to the image to be retrieved based on the extracted subject feature and non-subject feature;
  • this application proposes an image retrieval method.
  • the image to be retrieved is acquired and based on a preset
  • the feature extraction algorithm extracts subject features and non-subject features, and determines the corresponding image label through subject feature and non-subject feature extraction.
  • the feature extraction of the image to be retrieved includes subject feature extraction and non-subject feature extraction.
  • the subject is the subject with the subject, and the non-subject includes some special objects, such as landmark buildings. Therefore, the subject feature and non-subject feature of the image to be retrieved can be obtained through subject feature extraction and non-subject feature extraction.
  • the preset feature extraction algorithm used for extracting subject features and extracting non-subject features of the image to be retrieved specifically refers to Mask
  • the R-CNN algorithm the process of extracting the main features of the image to be retrieved through Mask R-CNN is as follows:
  • RPN Registered Candidate Network
  • the non-subject features in the image to be retrieved are also extracted using Mask
  • the difference between the R-CNN algorithm is that the non-subject feature is strengthened in the preprocessing process, and the non-subject feature map is extracted using the preset convolutional neural network, and the non-subject feature is finally determined through the RPN network Judgment and classification.
  • the Mask After the R-CNN algorithm extracts the subject feature and non-subject feature in the image to be retrieved it can further label the retrieved image according to the subject feature and non-subject feature.
  • the image tag includes the subject feature.
  • the label represents the image type corresponding to the image to be retrieved. Because of the existence of the subject and non-subject in the image to be retrieved, the image to be retrieved can be brought with There is more than one image tag.
  • Step S20 searching in a preset image database based on the image tag, and determining the image set corresponding to the image tag;
  • the step S20 specifically includes:
  • Step S21 acquiring a preset image tag corresponding to an image in a preset image database
  • a corresponding image database is pre-established, and the pictures in the preset image database have corresponding preset image tags. Specifically, in this embodiment, by acquiring a large number of samples For images, a corresponding image database is pre-established.
  • the image label corresponding to each sample image can be determined in advance, that is, the preset image label, so as to follow up with the image of the image to be retrieved.
  • the label is matched, and the determination of the preset image label can also be realized by the corresponding feature extraction algorithm, such as Mask R-CNN algorithm.
  • the preset image feature corresponding to the image of the preset image database can also be determined, so that the subsequent matching with the image feature of the image to be retrieved can more accurately determine the image to be retrieved The corresponding search result.
  • Step S22 matching the subject label and the preset image label, and determining the subject image set corresponding to the subject label;
  • Step S23 matching the non-subject label and the preset image label, and determine the non-subject image set corresponding to the non-subject label;
  • the R-CNN algorithm recognizes the subject characteristics of the image to be retrieved, and can tag the retrieved image with the corresponding subject label, and by recognizing the non-subject feature, the corresponding non-subject label can be marked.
  • the image set corresponding to the subject feature of the image to be retrieved can be obtained, and the image set corresponding to the non subject feature can be obtained by matching with the non-subject label.
  • the calculation of the label matching degree may set a corresponding matching degree threshold, and the images in the preset image database corresponding to the result of the matching degree calculation higher than the matching degree threshold are put into the corresponding image collection.
  • Step S24 Obtain the union of the subject image set and the non-subject image set, and determine it as the image set corresponding to the image tag.
  • the subject tag is calculated in the preset image database to determine the subject image set corresponding to the subject feature; the non subject feature is matched to obtain the image set corresponding to the non subject feature. It can be understood that these two images
  • the set may contain the same image or different images. Therefore, the union of the two image sets is taken as the image set corresponding to the image to be retrieved, so that the retrieval result can be further determined from the union.
  • Step S30 Determine the image feature of the image to be retrieved based on a preset image feature extraction algorithm, and perform feature matching in the image set according to the image feature to determine a candidate image corresponding to the image to be retrieved;
  • the preset image features corresponding to the images in the image database are determined, and further, through the preset image feature extraction algorithm, the image features of the image to be retrieved are extracted, based on the image of the image to be retrieved Feature: Perform feature matching calculation with the preset image features of the images contained in the image collection, and use the image with the feature matching degree, that is, the higher matching degree, as the candidate image corresponding to the image to be retrieved, further reducing image retrieval Range.
  • step S40 the candidate images are accurately sorted based on a preset sorting algorithm, and the candidate images are output in the sorted order.
  • n candidate images are further processed with high precision Reorder, and finally output the reordered n candidate images in order.
  • the preset sorting algorithm used for re-sorting n candidate images is the RANSAC algorithm.
  • the RANSAC algorithm randomly selects 4 samples from the matching data set (candidate images) and ensures that there is no difference between the 4 samples. Collinear, calculate the homography matrix corresponding to the 4 samples, record it as model M, then use this model to test all the data, and calculate the number of data points that meet this model and the projection error. If the corresponding projection error is the smallest, Then this model is the optimal model, and the candidate images are re-sorted according to the optimal model. Finally, the candidate images are output in the determined order, which is the retrieval result of the image to be retrieved.
  • the image to be retrieved is first obtained, and the corresponding image tag is determined based on the subject feature and non-subject feature of the image to be retrieved.
  • the image tag is used to search in the preset image database to determine the image tag corresponding And further determine the image features of the image to be retrieved based on the preset image feature extraction algorithm, so as to perform feature matching in the determined image set according to the image feature, determine the candidate image corresponding to the image to be retrieved, and finally
  • the candidate images are sorted accurately through a preset sorting algorithm, and the final sorted candidate images are output as the search result.
  • the image retrieval method proposed in this application determines image tags by extracting subject features and non-subject features to perform preliminary retrieval to narrow the retrieval range, and then accurately obtain retrieval results that match the image to be retrieved through image feature matching. Finally, The search results are reordered through the sorting algorithm to provide users with more accurate search results and improve the efficiency and accuracy of image search.
  • the step S30 specifically includes:
  • Step S31 Determine the image feature of the image to be retrieved based on a preset image feature extraction algorithm
  • the preset image features corresponding to the images in the image database are determined; when feature matching is required, the image features of the images to be retrieved are determined based on the preset image feature extraction algorithm.
  • the same image feature extraction algorithm can be used, specifically, it refers to the SIFT algorithm.
  • Image feature extraction mainly extracts multiple representative keywords (feature points) from the picture to form one Dictionary, and then count the number of keywords appearing in each picture to obtain the feature vector of the picture.
  • the process of constructing the preset image features in the preset image database is as follows:
  • the k-means clustering method is used to cluster all image features, and image features with the same feature are clustered into the same category. Specifically, k feature points are randomly selected as cluster centers, and other features are selected.
  • the distance between a point and K central points associate it with the closest central point, all points associated with the same central point form a cluster, and calculate the mean of each group of clusters to cluster the group
  • the associated center point moves to the position of the average value, and then repeats the steps of calculating the distance between other feature points and K center points so as to put them into the associated cluster to change the position of the cluster center point until The position of the center point does not change anymore, so that all the feature points are divided into different clusters.
  • the image that needs image feature extraction is convolved with a two-dimensional Gaussian function to obtain multiple Gaussian images with different ⁇ values.
  • These multiple Gaussian images with different ⁇ values constitute the scale space of the image, indicating that it is at different scales
  • the purpose of constructing the scale space is to detect the feature points that exist at different scales.
  • the image in order to improve the accuracy of feature point extraction, the image can be downsampled first to obtain image pyramids at different resolutions, and then Gaussian convolution is performed on each layer of the image. In this way, the original image Each layer of the pyramid has only one image, and after Gaussian convolution, images of different scales are added to each layer.
  • DOG Difference of Gaussian (Gaussian difference) to construct a Gaussian difference image
  • the DOG extreme point is further searched, that is, each pixel point is compared with the surrounding pixels.
  • adjacent points Includes pixels in the same scale space where the pixel is located and pixels in adjacent scale spaces.
  • Step S32 Obtain the preset image features corresponding to the images in the image collection from the preset image database, and perform feature evaluation on the image features and the preset image features corresponding to the images in the image collection The matching calculation determines a matching score, so as to determine the candidate image corresponding to the image to be retrieved based on the matching score.
  • a feature matching score corresponding to the image feature of and each preset image feature in the image set, and the candidate image corresponding to the image to be retrieved is determined based on the matching score.
  • a matching list can be established according to the level of the matching scores, the determined matching scores are sorted from high to low, and the preset number from the first in the matching list is selected in order
  • the matching score of takes several images in the corresponding image set as candidate images, that is, the search result corresponding to the image to be retrieved, and further uses the sorting algorithm to readjust the ranking of the candidate images to make the output search The result is more accurate.
  • the image feature of the image to be retrieved is determined by the preset image feature extraction algorithm, and the image feature corresponding to the image in the image set is determined Perform feature matching calculation on image features, determine the matching score, and finally determine the candidate images corresponding to the image to be retrieved, and perform high-precision sorting on the candidate images.
  • the sorted order is used as the final output retrieval result, which greatly improves the image The accuracy of retrieval.
  • the embodiment of the present application also provides an image retrieval device.
  • FIG. 5 is a schematic diagram of the functional modules of the first embodiment of the image retrieval device of this application.
  • the image retrieval device includes:
  • the feature extraction module 10 is configured to obtain the image to be retrieved, and perform subject feature extraction and non-subject feature extraction on the image to be retrieved, so as to determine the image label corresponding to the image to be retrieved based on the extracted subject feature and non-subject feature;
  • the tag retrieval module 20 is configured to retrieve in a preset image database based on the image tag, and determine the image set corresponding to the image tag;
  • the feature matching module 30 is configured to determine the image feature of the image to be retrieved based on a preset image feature extraction algorithm, and perform feature matching in the image set according to the image feature to determine the device corresponding to the image to be retrieved Select image
  • the image sorting module 40 is configured to accurately sort the candidate images based on a preset sorting algorithm, and output the candidate images in the sorted order.
  • the feature extraction module 10 specifically includes:
  • the request receiving unit is configured to obtain the image to be retrieved corresponding to the image retrieval request when the image retrieval request is received;
  • the feature extraction unit is configured to perform subject feature extraction and non-subject feature extraction on the image to be retrieved based on a preset feature extraction algorithm to determine subject features and non-subject features corresponding to the image to be retrieved, and based on the subject The feature and the non-subject feature determine the image tag corresponding to the image to be retrieved.
  • tag retrieval module 20 specifically includes:
  • a preset image tag acquiring unit configured to acquire a preset image tag corresponding to an image in a preset image database
  • a subject label matching unit configured to match the subject label with the preset image label, and determine the subject image set corresponding to the subject label
  • the non-subject tag matching unit is configured to match the non-subject tag with the preset image tag, and determine the non-subject image set corresponding to the non-subject tag;
  • the image collection acquisition unit is configured to acquire the union of the subject image collection and the non-subject image collection, and determine it as the image collection corresponding to the image tag.
  • the image retrieval device further includes:
  • An image database establishment unit for acquiring sample images and establishing a preset image database based on the sample images
  • the preset image tag determination unit is used to determine the preset image tag corresponding to the image in the preset image database, and determine the preset image tag corresponding to the image in the preset image database based on the preset image feature extraction algorithm. Set image characteristics.
  • the feature matching module 30 specifically includes:
  • An image feature determining unit configured to determine the image feature of the image to be retrieved based on a preset image feature extraction algorithm
  • the feature degree matching unit is configured to obtain a preset image feature corresponding to an image in the image collection from the preset image database, and compare the image feature with the preset image corresponding to the image in the image collection The feature performs feature matching calculation to determine a matching score, so as to determine the candidate image corresponding to the image to be retrieved based on the matching score.
  • the feature matching unit further includes:
  • the matching score sorting subunit is used to sort the matching scores from high to low to obtain a matching list, and select a preset number of matching scores from the top of the matching list, and set the preset The image in the image set corresponding to the number of matching scores is determined as the candidate image corresponding to the image to be retrieved.
  • each module in the above-mentioned image retrieval device corresponds to each step in the above-mentioned image retrieval method embodiment, and its functions and implementation processes will not be repeated here.
  • the embodiment of the present application also proposes a computer-readable storage medium, and the computer-readable storage medium may be a non-volatile readable storage medium.
  • the computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the image retrieval method described above are realized.
  • the solution provided in this embodiment first obtains the image to be retrieved, and extracts and determines the corresponding image label based on the subject feature and non-subject feature of the image to be retrieved, and uses the image tag to search in a preset image database to determine the image label Corresponding image set, and further determine the image feature of the image to be retrieved based on the preset image feature extraction algorithm, so as to perform feature matching in the determined image set according to the image feature to determine the candidate image corresponding to the image to be retrieved, Finally, the candidate images are accurately sorted by a preset sorting algorithm, and the final sorted candidate images are output as the retrieval result.
  • the image retrieval method proposed in this application determines image tags by extracting subject features and non-subject features to perform preliminary retrieval to narrow the retrieval range, and then accurately obtain retrieval results that match the image to be retrieved through image feature matching. Finally, The search results are reordered through the sorting algorithm to provide users with more accurate search results and improve the efficiency and accuracy of image search.
  • the method of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better. ⁇
  • the technical solution of this application essentially or the part that contributes to the existing technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM/RAM) as described above. , Magnetic disk, optical disk), including several instructions to make a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the method described in each embodiment of the present application.
  • a terminal device which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.

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Abstract

La présente invention appartient au domaine technique de la récupération d'image. L'invention concerne un procédé de récupération d'image, comprenant les étapes consistant à : acquérir une image à récupérer, et effectuer une extraction de caractéristique principale et une extraction de caractéristique non principale sur l'image à récupérer, de façon à déterminer, sur la base des caractéristiques principales et des caractéristiques non principales extraites, une étiquette d'image correspondant à l'image à récupérer; effectuer une récupération dans une base de données d'images prédéfinie sur la base de l'étiquette d'image, et déterminer un ensemble d'images correspondant à l'étiquette d'image; déterminer des caractéristiques de l'image à récupérer sur la base d'un algorithme d'extraction de caractéristiques d'image prédéfini, effectuer une mise en correspondance de caractéristiques dans l'ensemble d'images selon les caractéristiques d'image, et déterminer des images alternatives correspondant à l'image à récupérer; et effectuer un tri de précision sur les images alternatives sur la base d'un algorithme de tri prédéfini, et délivrer en sortie les images alternatives selon la séquence après le tri. L'invention concerne en outre un appareil et un dispositif de récupération d'image, ainsi qu'un support de stockage lisible par ordinateur. Selon la présente invention, la précision de récupération d'image est améliorée.
PCT/CN2019/117191 2019-03-16 2019-11-11 Procédé, appareil et dispositif de récupération d'image et support de stockage lisible par ordinateur WO2020186777A1 (fr)

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