CN114998599A - Data processing method, device, equipment and computer readable medium - Google Patents
Data processing method, device, equipment and computer readable medium Download PDFInfo
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Abstract
The application discloses a data processing method, a device, equipment and a computer readable medium, which relate to the technical field of computers, and the method comprises the following steps: acquiring image data and extracting image characteristics corresponding to the image data; clustering image features to obtain clustering clusters, and dividing image data according to the clustering clusters to obtain first image data and second image data; randomly sampling the first image data based on an orthogonal basis to obtain corresponding sparse data; storing the second image data and the sparse data; and responding to the detection of the calling operation of the user on the stored second image data and the sparse data, executing a data reconstruction process based on the sparse data to obtain reconstructed first image data, combining the reconstructed first image data with the second image data to obtain restored image data and outputting the restored image data. The method and the device have the advantages that the storage pressure of the image data is reduced, the storage space occupied by the image data is reduced, meanwhile, the image can be reconstructed with high quality, and the problem of overlarge storage pressure of storage equipment is relieved to a great extent.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a data processing method, apparatus, device, and computer readable medium.
Background
With the development of electronic modernization technology becoming faster and faster, the number of electronic images grows exponentially, pixels on the electronic images are recorded in the form of digital codes, image files comprise uncompressed formats and compressed formats such as IPEG and GIF, all data of the images are stored in an electronic license management system, although the image fidelity is good, the data size is huge, and huge pressure is brought to computer storage.
In the process of implementing the present application, the inventor finds that at least the following problems exist in the prior art:
the image file comprises an uncompressed format and a compressed format such as IPEG and GIF, all data of the image are stored in the license management system, the data volume is huge, and the image occupies too large storage space.
Disclosure of Invention
In view of this, embodiments of the present application provide a data processing method, an apparatus, a device, and a computer readable medium, which can solve the problems that an existing image file includes an uncompressed format and a compressed format such as IPEG and GIF, all data of an image needs to be stored in a license management system, the data size is huge, and the image occupation and storage are too large.
To achieve the above object, according to an aspect of an embodiment of the present application, there is provided a data processing method including:
acquiring image data, and further extracting image features corresponding to the image data;
clustering image features to obtain clustering clusters, and dividing image data according to the clustering clusters to obtain first image data and second image data;
randomly sampling the first image data based on an orthogonal basis to obtain corresponding sparse data;
storing the second image data and the sparse data;
and responding to the detection of the calling operation of the user on the stored second image data and the sparse data, executing a data reconstruction process based on the sparse data to obtain reconstructed first image data, and combining the reconstructed first image data with the second image data to obtain restored image data and outputting the restored image data.
Optionally, clustering the image features to obtain each cluster, including:
and clustering image features with similarity exceeding a preset threshold in different image data into one class, and further obtaining each cluster.
Optionally, dividing the image data according to each cluster to obtain the first image data and the second image data includes:
dividing the image data corresponding to each cluster into second image data;
first image data is determined from the image data and the second image data.
Optionally, randomly sampling the first image data based on an orthogonal basis to obtain corresponding sparse data, including:
performing region division on the first image data to obtain region division data;
and carrying out random sampling on the region division data based on the orthogonal basis to obtain corresponding sparse data.
Optionally, extracting image features corresponding to the image data includes:
and extracting the high-level features and the low-level features corresponding to the image data.
Optionally, clustering the image features to obtain each cluster, including:
fusing the high-level features and the low-level features to obtain fused features;
and clustering the fusion characteristics to obtain corresponding clustering clusters.
Optionally, fusing the high-level features and the low-level features to obtain fused features, including:
acquiring a first confidence score corresponding to the high-level features and a second confidence score corresponding to the low-level features;
and fusing the high-level features and the low-level features based on the first confidence score and the second confidence score to obtain fused features.
In addition, the present application also provides a data processing apparatus, including:
the acquisition unit is configured to acquire the image data and further extract image features corresponding to the image data;
the image data dividing unit is configured to cluster the image features to obtain each cluster, and further divide the image data according to each cluster to obtain first image data and second image data;
a sampling unit configured to randomly sample the first image data based on an orthogonal basis to obtain corresponding sparse data;
a storage unit configured to store the second image data and the sparse data;
and the output unit is configured to respond to the detection of the calling operation of the user on the stored second image data and the sparse data, execute a data reconstruction process based on the sparse data to obtain reconstructed first image data, and further combine the first image data and the second image data to obtain restored image data and output the restored image data.
Optionally, the image data dividing unit is further configured to:
and clustering image features with similarity exceeding a preset threshold in different image data into one class, and further obtaining each cluster.
Optionally, the image data dividing unit is further configured to:
dividing the image data corresponding to each cluster into second image data;
first image data is determined from the image data and the second image data.
Optionally, the sampling unit is further configured to:
performing region division on the first image data to obtain region division data;
and randomly sampling the region division data based on an orthogonal basis to obtain corresponding sparse data.
Optionally, the obtaining unit is further configured to:
and extracting the high-level features and the low-level features corresponding to the image data.
Optionally, the image data dividing unit is further configured to:
fusing the high-level features and the low-level features to obtain fused features;
and clustering the fusion characteristics to obtain corresponding clustering clusters.
Optionally, the image data dividing unit is further configured to:
acquiring a first confidence score corresponding to the high-level features and a second confidence score corresponding to the low-level features;
and fusing the high-level features and the low-level features based on the first confidence score and the second confidence score to obtain fused features.
In addition, the present application also provides a data processing apparatus, including: one or more processors; a storage device for storing one or more programs which, when executed by one or more processors, cause the one or more processors to implement the data processing method as described above.
In addition, the present application also provides a computer readable medium, on which a computer program is stored, which when executed by a processor implements the data processing method as described above.
To achieve the above object, according to still another aspect of embodiments of the present application, there is provided a computer program product.
A computer program product according to an embodiment of the present application includes a computer program, and when the computer program is executed by a processor, the computer program implements the data processing method according to an embodiment of the present application.
One embodiment of the above invention has the following advantages or benefits: according to the method, image characteristics corresponding to the image data are extracted by acquiring the image data; clustering image features to obtain clustering clusters, and dividing image data according to the clustering clusters to obtain first image data and second image data; randomly sampling the first image data based on an orthogonal basis to obtain corresponding sparse data; storing the second image data and the sparse data; and responding to the detection of the calling operation of the user on the stored second image data and the sparse data, executing a data reconstruction process based on the sparse data to obtain reconstructed first image data, and combining the reconstructed first image data with the second image data to obtain restored image data and outputting the restored image data. The method and the device have the advantages that the storage pressure of the image data is reduced, the storage space occupied by the image data is reduced, meanwhile, the image can be reconstructed with high quality, and the problem of overlarge storage pressure of the storage device is relieved to a great extent.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a further understanding of the application and are not to be construed as limiting the application. Wherein:
FIG. 1 is a schematic diagram of a main flow of a data processing method according to one embodiment of the present application;
FIG. 2 is a schematic diagram of a main flow of a data processing method according to one embodiment of the present application;
FIG. 3 is a schematic main flow diagram of a data processing method according to an embodiment of the present application;
FIG. 4 is a schematic diagram of the main elements of a data processing apparatus according to an embodiment of the present application;
FIG. 5 is an exemplary system architecture diagram to which embodiments of the present application may be applied;
fig. 6 is a schematic structural diagram of a computer system suitable for implementing the terminal device or the server according to the embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness. According to the technical scheme, the data acquisition, storage, use, processing and the like meet the relevant regulations of national laws and regulations.
Fig. 1 is a schematic diagram of a main flow of a data processing method according to an embodiment of the present application, and as shown in fig. 1, the data processing method includes:
step S101, image data is obtained, and then image features corresponding to the image data are extracted.
In this embodiment, an execution subject (for example, a server) of the data processing method may acquire image data by wired connection or wireless connection. The image data may be, for example, an electronic photograph. The format of the electronic photo can be a compression format such as IPEG, GIF and the like. The execution subject can record and save the pixels in the acquired image data in the form of digital codes so as to realize the processing of the acquired image data.
After the execution subject acquires the image data, the execution subject may further extract image features corresponding to the image data. Specifically, the executing subject may input the acquired image data to a feature extraction model, such as a CNN model, and extract image features of the input image data using a convolutional layer and a pooling layer of the CNN model. The image features include color features, texture features, shape features, spatial relationship features, and the like. Wherein, the color feature is a global feature describing surface properties of a scene corresponding to the image or the image area; texture features are also global features that also describe the surface properties of the scene corresponding to the image or image area; the shape features are represented by two types, one is outline features, the other is region features, the outline features of the image mainly aim at the outer boundary of the object, and the region features of the image are related to the whole shape region; the spatial relationship feature refers to a spatial position or a relative direction relationship between a plurality of objects segmented from an image, and these relationships can be classified into a connection/adjacency relationship, an overlapping/overlapping relationship, an inclusion/containment relationship, and the like.
Step S102, clustering image features to obtain each cluster, and further dividing image data according to each cluster to obtain first image data and second image data.
In the embodiment of the application, the acquired image data may include one or more images, and when the acquired image data includes a plurality of images, the execution main body may compare image features extracted from the plurality of images based on color and texture, and then cluster the image features having the same or similar comparison results to obtain each cluster. Then, the execution subject may divide the image data based on the respective cluster clusters to obtain the first image data and the second image data. Specifically, the first image data may be a part of one image (e.g., a1), and the second image data may be another part of the one image (e.g., a2, where a1+ a2 may constitute an entire image).
Specifically, clustering image features to obtain each cluster includes:
and clustering image features with similarity exceeding a preset threshold in different image data into one class, and further obtaining each cluster.
The execution subject may group similar portions in each image corresponding to the acquired image data into one type (that is, group image features having a similarity exceeding a preset threshold value in different images into one type), group dissimilar portions into another type (that is, group image features having a similarity lower than the preset threshold value into another type), and then obtain each cluster.
Specifically, dividing the image data according to each cluster to obtain first image data and second image data includes:
and dividing the image data corresponding to each cluster into second image data, namely image data corresponding to similar parts. The image data forms a cluster, that is, the image data indicates that each image corresponding to the image data has a similar portion, the similar portion can be divided into second image data, and other data except the second image data in each image can be divided into first image data, that is, image data corresponding to the dissimilar portion.
First image data is determined from the image data and the second image data.
The execution subject may divide each image corresponding to the acquired image data into two parts, one part is image data corresponding to the cluster, that is, second image data, and the other part is image data in each image except for the image data corresponding to the cluster, that is, first image data.
Step S103, random sampling is carried out on the first image data based on the orthogonal basis to obtain corresponding sparse data.
Specifically, randomly sampling the first image data based on an orthogonal basis to obtain corresponding sparse data includes:
the first image data is subjected to region division to obtain region division data. The execution subject may perform region division on image data (i.e., first image data) corresponding to the dissimilar portions, resulting in respective divided regions (i.e., region divided data). And randomly sampling the region division data based on an orthogonal basis to obtain corresponding sparse data. The orthogonal basis is a sparse basis used when the compressive sensing is applied to image processing, that is, when data processing is performed on an image by using a compressive sensing theory, the sparse basis used is the orthogonal basis, the inner product of any two different bases in the orthogonal basis is 0, calculation is greatly simplified, and random sampling is performed by using the orthogonal basis in random sampling, so that sparsity can be increased, and data can be concentrated.
Compressed sensing, i.e. the signal is sparse or sparse in the domain of variation, can be used to project the high-dimensional signal onto a low-dimensional space using an observation matrix that is not related to the sparse basis, and the original signal can be reconstructed with high probability using these projections by solving an optimization problem. Applying compressed sensing to data processing for an image may comprise the following steps: a. searching a proper sparse base; b. designing an incoherent sensing matrix; c. and constructing a reconstruction algorithm. The compressed sensing can reduce the collection quantity of the electronic photos, reduce the data storage pressure, reconstruct the electronic photos with high quality and greatly relieve the problem of overlarge storage pressure of the storage equipment.
And step S104, storing the second image data and the sparse data.
The execution subject may store the second image data (i.e., image data of a similar portion in each image) and the sparse data after obtaining the sparse data corresponding to the first image data. The data can be stored locally or in a cloud terminal, and the storage position is not particularly limited in the embodiment of the application.
Step S105, in response to detecting that the user invokes the stored second image data and the sparse data, performing a data reconstruction process based on the sparse data to obtain reconstructed first image data, and then combining the first image data with the second image data to obtain restored image data and outputting the restored image data.
When the execution subject detects that the user calls the stored second image data and the sparse data, it can be indicated that the user needs to view the original image corresponding to the second image data and the sparse data, the execution subject can reconstruct the sparse data by using an OMP algorithm to obtain corresponding first image data, and then the execution subject can splice the reconstructed first image data and the called second image data to obtain restored image data and output and display the restored image data.
In the embodiment, image characteristics corresponding to image data are extracted by acquiring the image data; clustering image features to obtain clustering clusters, and dividing image data according to the clustering clusters to obtain first image data and second image data; randomly sampling the first image data based on an orthogonal basis to obtain corresponding sparse data; storing the second image data and the sparse data; and responding to the detection of the calling operation of the user on the stored second image data and the sparse data, executing a data reconstruction process based on the sparse data to obtain reconstructed first image data, and combining the reconstructed first image data with the second image data to obtain restored image data and outputting the restored image data. The method and the device have the advantages that the storage pressure of the image data is reduced, the storage space occupied by the image data is reduced, meanwhile, the image can be reconstructed with high quality, and the problem of overlarge storage pressure of the storage device is relieved to a great extent.
Fig. 2 is a schematic main flow diagram of a data processing method according to an embodiment of the present application, and as shown in fig. 2, the data processing method includes:
step S201, acquiring image data, and extracting a high-level feature and a low-level feature corresponding to the image data.
The high-level features, for example, when the image data corresponds to a face image, the high-level features may be a face, and the low-level features may be contours, noses, eyes, etc. The execution main body can input the acquired image data into the feature extraction model to call a deep neural network in the feature extraction model to extract high-level features, call a shallow neural network in the feature extraction model to extract low-level features, and can output confidence scores of the high-level features and the shallow features. Wherein the confidence score is used for representing the credibility of the feature output by the feature extraction model.
And step S202, fusing the high-level features and the low-level features to obtain fused features.
The feature fusion method can comprehensively utilize the high-level features and the low-level features, and realize the advantage complementation of the multiple features so as to obtain a more robust and accurate clustering result.
Specifically, fusing the high-level features and the low-level features to obtain fused features, including:
acquiring a first confidence score corresponding to the high-level features and a second confidence score corresponding to the low-level features; the execution subject may obtain a confidence score corresponding to the higher-level feature, i.e., a first confidence score, from the output of the feature extraction model, and may obtain a confidence score corresponding to the lower-level feature, i.e., a second confidence score, from the output of the feature extraction model.
And fusing the high-level features and the low-level features based on the first confidence score and the second confidence score to obtain fused features.
The executive agent may convert the high-level features into a high-level feature vector by word embedding, for example, a, use the first confidence score as a weight of the high-level feature vector, for example, x1, convert the low-level features into a low-level feature vector b by word embedding, use the second confidence score as a weight of the low-level feature vector, for example, x2, and obtain a fused feature c, for example, c ═ x1+ b x2, where × represents the meaning of multiplication, a × 1 means the product of a and x1, and b × 2 means the product of b and x 2.
And step S203, clustering the fusion features to obtain corresponding clustering clusters.
The execution subject can perform clustering on the fused features obtained after fusion according to the similarity between the fused features, and the fused features with the similarity exceeding the threshold value are clustered into one class, so that each cluster is obtained. By clustering based on the fusion characteristics, the obtained clustering clusters can be more accurate, and thus a foundation is laid for accurate division of images.
And S204, dividing the image data according to each cluster to obtain first image data and second image data.
And dividing the acquired image data according to the clustering cluster, and dividing each image corresponding to the image data into a part consistent with other images and a part inconsistent with other images, which are respectively called as first image data and second image data. The other images are images other than the currently processed image among the images corresponding to the image data. That is, the acquired image data is determined corresponding to the consistent part of each image, and the inconsistent part is also determined, wherein the consistent part is called as the second image data, and the inconsistent part is called as the first image data. The image data is divided through the clustering cluster obtained based on the fusion characteristics, so that the divided image data can be more accurate.
Step S205, randomly sampling the first image data based on the orthogonal basis to obtain corresponding sparse data.
Step S206, the second image data and the sparse data are stored.
Step S207, in response to detecting that the user invokes the stored second image data and the sparse data, performing a data reconstruction process based on the sparse data to obtain reconstructed first image data, and then combining the first image data with the second image data to obtain restored image data and outputting the restored image data.
The embodiment of the application can reduce the storage pressure of the image data, reduce the storage space occupied by the image data, reconstruct the image with high quality and greatly relieve the problem of overlarge storage pressure of the storage equipment.
Fig. 3 is a schematic view of an application scenario of a data processing method according to an embodiment of the present application. The data processing method of the embodiment of the application can be applied to scenes for storing images. As shown in fig. 3, in storing images, many images have similar parts, and the unchanged image parts in the image template data are first stored in the database in full. The area division is performed for the inconsistent portions in the image, that is, the image variable data. Random sampling is carried out on the inconsistent part in the image, about 5% of data can be collected, and random sampling can be carried out by using an orthogonal base in the random sampling, so that the sparsity can be increased, and the data obtained by the random sampling is more concentrated. And storing the image data by using the acquired sparse data. And reconstructing the compressed image data by utilizing the improved OMP algorithm to restore the image data for a user to check.
According to the embodiment of the application, the electronic photos are stored by using the electronic license system, a regular computer database is established, similar photos are only needed to be stored, and for inconsistent parts in the images, although the data storage amount is reduced to a certain extent compared with the previous part, random sampling can be performed by using a compressed sensing mode, so that the data storage amount is further reduced.
When the stored image is an electronic certificate, the characteristic that part of the electronic photos have similarity, such as the electronic certificates of identity card photos and business licenses, is utilized, firstly, the similar information of the electronic photos is stored, and the dissimilar parts in the photos are stored in a compressed sensing mode, so that on the premise of ensuring that the photos can be restored, the data storage capacity can be greatly reduced, and the pressure of storage equipment is effectively relieved. With the popularization of electronic certificates, more electronic photos need to be stored, higher requirements are also placed on hardware facilities and storage space, in order to relieve the storage pressure of equipment, the similarity of partial electronic photos is utilized, the consistent parts in images are stored in full, and the data volume to be stored is reduced again by adopting a compressed sensing mode for the inconsistent parts, so that the storage pressure of the equipment is effectively relieved.
Fig. 4 is a schematic diagram of main units of a data processing apparatus according to an embodiment of the present application. As shown in fig. 4, the data processing apparatus 400 includes an acquisition unit 401, an image data dividing unit 402, a sampling unit 403, a storage unit 404, and an output unit 405.
An obtaining unit 401 is configured to obtain image data, and further extract image features corresponding to the image data.
The image data dividing unit 402 is configured to cluster the image features to obtain cluster clusters, and further divide the image data according to the cluster clusters to obtain first image data and second image data.
A sampling unit 403 configured to randomly sample the first image data based on the orthogonal basis to obtain corresponding sparse data.
A storage unit 404 configured to store the second image data and the sparse data.
An output unit 405 configured to, in response to detecting a user's call operation on the stored second image data and the sparse data, perform a data reconstruction process based on the sparse data to obtain reconstructed first image data, and further combine with the second image data to obtain restored image data and output the restored image data.
In some embodiments, the image data dividing unit 402 is further configured to: and clustering image features with similarity exceeding a preset threshold in different image data into one class, and further obtaining each cluster.
In some embodiments, the image data dividing unit 402 is further configured to: dividing the image data corresponding to each cluster into second image data; first image data is determined from the image data and the second image data.
In some embodiments, the sampling unit 403 is further configured to: performing region division on the first image data to obtain region division data; and carrying out random sampling on the region division data based on the orthogonal basis to obtain corresponding sparse data.
In some embodiments, the obtaining unit 401 is further configured to: and extracting the high-level features and the low-level features corresponding to the image data.
In some embodiments, the image data dividing unit 402 is further configured to: fusing the high-level features and the low-level features to obtain fused features; and clustering the fusion characteristics to obtain corresponding clustering clusters.
In some embodiments, the image data dividing unit 402 is further configured to: acquiring a first confidence score corresponding to the high-level features and a second confidence score corresponding to the low-level features; and fusing the high-level features and the low-level features based on the first confidence score and the second confidence score to obtain fused features.
It should be noted that, in the present application, the data processing method and the data processing apparatus have corresponding relation in the specific implementation contents, and therefore, the repeated contents are not described again.
Fig. 5 shows an exemplary system architecture 500 to which the data processing method or the data processing apparatus of the embodiments of the present application may be applied.
As shown in fig. 5, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505. The network 504 serves to provide a medium for communication links between the terminal devices 501, 502, 503 and the server 505. Network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 501, 502, 503 to interact with a server 505 over a network 504 to receive or send messages or the like. The terminal devices 501, 502, 503 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 501, 502, 503 may be various devices having data processing screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 505 may be a server that provides various services, such as a background management server (for example only) that provides support for image data acquired by users using the terminal devices 501, 502, 503. The background management server can acquire the image data and further extract the image characteristics corresponding to the image data; clustering image features to obtain clustering clusters, and dividing image data according to the clustering clusters to obtain first image data and second image data; randomly sampling the first image data based on an orthogonal basis to obtain corresponding sparse data; storing the second image data and the sparse data; and responding to the detection of the calling operation of the user on the stored second image data and the sparse data, executing a data reconstruction process based on the sparse data to obtain reconstructed first image data, and combining the reconstructed first image data with the second image data to obtain restored image data and outputting the restored image data. The method and the device have the advantages that the storage pressure of the image data is reduced, the storage space occupied by the image data is reduced, meanwhile, the image can be reconstructed with high quality, and the problem of overlarge storage pressure of the storage device is relieved to a great extent.
It should be noted that the data processing method provided in the embodiment of the present application is generally executed by the server 505, and accordingly, the data processing apparatus is generally disposed in the server 505.
It should be understood that the number of terminal devices, networks, and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use in implementing a terminal device of an embodiment of the present application. The terminal device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the computer system 600 are also stored. The CPU601, ROM602, and RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output section 607 including a signal processing section such as a Cathode Ray Tube (CRT), a liquid crystal credit authorization inquiry processor (LCD), and the like, and a speaker and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to embodiments disclosed herein, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments disclosed herein include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the present application are executed when the computer program is executed by the Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes an acquisition unit, an image data dividing unit, a sampling unit, a storage unit, and an output unit. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to acquire image data and further extract image features corresponding to the image data; clustering image features to obtain clustering clusters, and dividing image data according to the clustering clusters to obtain first image data and second image data; randomly sampling the first image data based on an orthogonal basis to obtain corresponding sparse data; storing the second image data and the sparse data; and responding to the detection of the calling operation of the user on the stored second image data and the sparse data, executing a data reconstruction process based on the sparse data to obtain reconstructed first image data, and combining the reconstructed first image data with the second image data to obtain restored image data and outputting the restored image data.
The computer program product of the present application comprises a computer program which, when executed by a processor, implements the data processing method of the embodiments of the present application.
According to the technical scheme of the embodiment of the application, the storage pressure of the image data can be reduced, the storage space occupied by the image data is reduced, meanwhile, the image can be reconstructed with high quality, and the problem of overlarge storage pressure of the storage equipment is relieved to a great extent.
The above-described embodiments are not intended to limit the scope of the present disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (16)
1. A data processing method, comprising:
acquiring image data, and further extracting image features corresponding to the image data;
clustering the image features to obtain each cluster, and dividing the image data according to each cluster to obtain first image data and second image data;
randomly sampling the first image data based on an orthogonal basis to obtain corresponding sparse data;
storing the second image data and the sparse data;
and responding to the detection of the calling operation of the user on the stored second image data and the stored sparse data, executing a data reconstruction process based on the sparse data to obtain reconstructed first image data, and combining the reconstructed first image data with the second image data to obtain restored image data and outputting the restored image data.
2. The method of claim 1, wherein clustering the image features to obtain clusters comprises:
and clustering the image features with the similarity exceeding a preset threshold in different image data into one class to further obtain each cluster.
3. The method of claim 1, wherein the partitioning the image data according to the clusters to obtain first image data and second image data comprises:
dividing the image data corresponding to each cluster into second image data;
determining first image data according to the image data and the second image data.
4. The method of claim 1, wherein randomly sampling the first image data based on an orthogonal basis to obtain corresponding sparse data comprises:
performing region division on the first image data to obtain region division data;
and carrying out random sampling on the region division data based on an orthogonal basis to obtain corresponding sparse data.
5. The method according to claim 1, wherein the extracting image features corresponding to the image data comprises:
and extracting the high-level features and the low-level features corresponding to the image data.
6. The method of claim 5, wherein clustering the image features to obtain clusters comprises:
fusing the high-level features and the low-level features to obtain fused features;
and clustering the fusion characteristics to obtain corresponding clustering clusters.
7. The method of claim 6, wherein fusing the high-level features and the low-level features to obtain fused features comprises:
acquiring a first confidence score corresponding to the high-level features and a second confidence score corresponding to the low-level features;
fusing the high-level features and the low-level features based on the first confidence score and the second confidence score to obtain fused features.
8. A data processing apparatus, comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is configured to acquire image data and further extract image characteristics corresponding to the image data;
the image data dividing unit is configured to cluster the image features to obtain each cluster, and further divide the image data according to each cluster to obtain first image data and second image data;
a sampling unit configured to randomly sample the first image data based on an orthogonal basis to obtain corresponding sparse data;
a storage unit configured to store the second image data and the sparse data;
and the output unit is configured to respond to the detection of the calling operation of the user on the stored second image data and the sparse data, execute a data reconstruction process based on the sparse data to obtain reconstructed first image data, and further combine the reconstructed first image data with the second image data to obtain restored image data and output the restored image data.
9. The apparatus of claim 8, wherein the image data partitioning unit is further configured to:
and clustering image features with similarity exceeding a preset threshold in different image data into one class, and further obtaining each cluster.
10. The apparatus of claim 8, wherein the image data partitioning unit is further configured to:
dividing the image data corresponding to each cluster into second image data;
determining first image data according to the image data and the second image data.
11. The apparatus of claim 8, wherein the sampling unit is further configured to:
performing region division on the first image data to obtain region division data;
and carrying out random sampling on the region division data based on an orthogonal basis to obtain corresponding sparse data.
12. The apparatus of claim 8, wherein the obtaining unit is further configured to:
and extracting the high-level features and the low-level features corresponding to the image data.
13. The apparatus of claim 12, wherein the image data partitioning unit is further configured to:
fusing the high-level features and the low-level features to obtain fused features;
and clustering the fusion characteristics to obtain corresponding clustering clusters.
14. A data processing apparatus, characterized by comprising:
one or more processors;
a storage device for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-7.
15. A computer-readable medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method of any one of claims 1-7.
16. A computer program product comprising a computer program, characterized in that the computer program realizes the method according to any of claims 1-7 when executed by a processor.
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