WO2019137259A1

WO2019137259A1 - Image processing method and apparatus, storage medium, and electronic device

Info

Publication number: WO2019137259A1
Application number: PCT/CN2018/125451
Authority: WO
Inventors: 陈岩; 刘耀勇
Original assignee: Oppo广东移动通信有限公司
Priority date: 2018-01-10
Filing date: 2018-12-29
Publication date: 2019-07-18
Also published as: CN110022397A; CN110022397B

Abstract

Disclosed is an image processing method. The method comprises: acquiring an image in an album, and performing image analysis on the image; determining a sample label corresponding to the image according to the image analysis result; classifying the image on the basis of the sample label, and moving the image to a corresponding classification library; and compressing the resolution of the image in the classification library according to a preset rule. Thus, the utilization efficiency of the storage space of an electronic device is improved.

Description

Image processing method, device, storage medium and electronic device

This application claims priority to Chinese Patent Application No. 201810024319.6, entitled "Image Processing Method, Apparatus, Storage Medium and Electronic Equipment", filed on January 10, 2018, the entire contents of which are hereby incorporated by reference. The citations are incorporated herein by reference.

Technical field

The present application relates to the field of image processing technologies, and in particular, to an image processing method, apparatus, storage medium, and electronic device.

Background technique

At present, with the rapid development of terminal technologies, such as smart phones more and more people's lives, users often install a large number of applications on smartphones, such as camera applications, game applications, map applications and so on.

After the user takes a picture through the camera application or saves the picture in other applications, the picture exists in the storage space of the smart phone. Due to the current camera resolution of the smart phone and the screen display resolution, the resolution corresponding to the picture is increased. It is also getting higher and higher, and the storage space is also getting larger and larger, resulting in excessive storage space and lowering the running speed of the smartphone.

Summary of the invention

The embodiment of the present application provides an image processing method, device, storage medium, and electronic device, which can improve the utilization efficiency of the storage space of the electronic device.

In a first aspect, an embodiment of the present application provides an image processing method, including:

Acquiring images in the album and performing image analysis on the images;

Determining a sample label corresponding to the image according to an image analysis result;

Performing classification processing on the image based on the sample tag, and moving the image into a corresponding classification library;

The resolution of the image in the classification library is compressed according to a preset rule.

In a second aspect, an image processing apparatus provided by the embodiment of the present application includes:

An analyzing unit, configured to acquire an image in an album, and perform image analysis on the image;

a label determining unit, configured to determine a sample label corresponding to the image according to the image analysis result;

a moving unit, configured to perform classification processing on the image based on the sample label, and move the image into a corresponding classification library;

And a processing unit, configured to perform compression processing on a resolution of the image in the classification library according to a preset rule.

In a third aspect, a storage medium provided by an embodiment of the present application has a computer program stored thereon, and when the computer program is run on a computer, the computer is caused to perform an image processing method according to any embodiment of the present application.

In a fourth aspect, an embodiment of the present application provides an electronic device, including a processor and a memory, where the memory has a computer program, and the processor is configured to perform the steps by calling the computer program:

Acquiring images in the album and performing image analysis on the images;

DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the present invention. Other drawings can also be obtained from those skilled in the art based on these drawings without paying any creative effort.

FIG. 1 is a schematic flowchart of an image processing method provided by an embodiment of the present application.

FIG. 2 is another schematic flowchart of an image processing method provided by an embodiment of the present application.

FIG. 3 is a schematic diagram of an application scenario of an image processing method according to an embodiment of the present disclosure.

FIG. 4 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present application.

FIG. 5 is another schematic structural diagram of an image processing apparatus according to an embodiment of the present application.

FIG. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

FIG. 7 is another schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed ways

Referring to the drawings, wherein like reference numerals represent the same components, the principles of the present application are illustrated by the implementation in a suitable computing environment. The following description is based on the specific embodiments of the present invention as illustrated, and should not be construed as limiting the specific embodiments that are not described herein.

The embodiment of the present application provides an image processing method, and the execution body of the image processing method may be an image processing device provided by an embodiment of the present application, or an electronic device integrated with the image processing device, where the image processing device may adopt hardware or The way the software is implemented. The electronic device may be a device such as a smart phone, a tablet computer, a palmtop computer, a notebook computer, or a desktop computer.

An embodiment of the present invention provides an image processing method, including:

Obtain an image in the album and perform image analysis on the image;

Determining a sample label corresponding to the image according to the image analysis result;

And classifying the image based on the sample label, and moving the image to a corresponding classification library;

In an embodiment, the step of acquiring an image in an album and performing image analysis on the image may include: acquiring an image in the album, and extracting a plurality of features of the image by using a convolutional neural network; The plurality of features generate a corresponding plurality of entity words; the plurality of features and the plurality of entity words are embedded and processed to generate a corresponding entity description.

In an embodiment, the step of determining a sample tag corresponding to the image according to the image analysis result may include: performing an intelligent reorganization of the entity description corresponding to the image, generating a recombined entity description; and describing the reorganized entity Analyze and determine the corresponding sample label.

In an embodiment, the step of classifying the image based on the sample label, and moving the image to the corresponding classification library may include: acquiring a keyword indicated by the sample label; and obtaining a corresponding indication of the classification library Sorting a keyword; determining whether the keyword matches the classified keyword; and when determining that the keyword matches the classified keyword, moving the image into the classified library.

In an embodiment, after the step of determining whether the keyword matches the classified keyword, the method may further include: when determining that the keyword does not match the classified keyword, generating prompt information to prompt the user to The image is classified; or when it is determined that the keyword does not match the classified keyword, the similarity between the keyword and the classified keyword is obtained, and the image is moved to the classification database with the highest similarity.

In an embodiment, before the step of compressing the resolution of the image in the classification library according to the preset rule, the method may further include: acquiring an image in the classification library, and generating a thumbnail image of all the images and performing a list. Displaying; generating prompt information for indicating whether the user performs compression processing on the image; receiving an operation instruction input by the user according to the prompt information, the operation instruction indicating a target image that needs to be subjected to compression processing.

In an embodiment, the step of compressing the resolution of the image in the classification library according to the preset rule may include: compressing the resolution of the target image in the classification library according to a preset rule.

Please refer to FIG. 1. FIG. 1 is a schematic flowchart diagram of an image processing method according to an embodiment of the present application. The specific process of the image processing method provided by the embodiment of the present application may be as follows:

201. Acquire an image in the album and perform image analysis on the image.

The image analysis mentioned in the embodiment is technically completed based on Image Caption, which is a comprehensive problem of integrating computer vision, natural language processing and machine learning. Describe the image as accurately as possible.

The image in the album includes all images stored by the electronic device, and the image may be an image in multiple formats, such as Bmp, jpg, png, tiff, gif, pcx, tga, exif, fpx, svg, psd, cdr, Pdd, dxf, ufo, eps, ai, raw, WMF and other formats.

It should be noted that the Convolutional Neural Network (CNN) is a feedforward neural network whose artificial neurons can respond to surrounding units in a part of coverage and have excellent performance for large image processing.

In an embodiment, the features of the image are acquired by the convolutional neural network, and corresponding entity words are generated according to the features, for example, the convolutional neural network acquires the features of the characters in the image, and the entity words can be generated according to the characteristics of the characters. For people.

Further, the features and entity words obtained by the convolutional neural network are embedded in the same space, and then the optimal description is selected to generate an entity description. Through the description of the entity, the image can be initially recognized so that the image can be classified later.

202. Determine a sample label corresponding to the image according to the image analysis result.

The sample label is a general description of the image, and the function of the image can be known through the sample label.

In an embodiment, the entity description in step 201 may be reorganized through a certain grammar database to generate a grammatically fluent entity description to summarize the sample tags of the image.

Further, by analyzing the reorganized entity description, a sample tag in which the keyword is determined is extracted.

203. Perform classification processing on the image based on the sample label, and move the image to the corresponding classification library.

In an embodiment, a plurality of classification libraries may be established, and each classification library corresponds to a different classification keyword. For example, the character classification library, the classification keywords corresponding to the character classification library are people, faces, eyes, and the body. And the file type classification library, the classification keywords corresponding to the file type classification library are characters, files, formulas, and the like. The classification library can also include multiple types, which are not described here.

Wherein, by respectively obtaining the keyword indicated by the sample tag and the classification keyword corresponding to the classification library, it is determined whether the two match, and when it is determined that the two match, the image is moved into the classification library, and when the two are determined If there is no match, continue to obtain the classification keyword corresponding to the other classification library.

In an embodiment, when it is detected that the keyword indicated by the sample label does not match the classification keyword indicated by all the classification libraries, the prompt information may be generated to prompt the user to manually classify the image, or may be based on keywords. The similarity is matched, and the image is moved to a classification library with the highest keyword similarity matching value.

204. Perform compression processing on the resolution of the image in the classification library according to a preset rule.

Understandably, some images do not have to use too high resolution due to the purpose of use, such as text-like images, only the user can see the text in the image.

Among them, according to the preset rule, the resolution of the images in different classification libraries is compressed, which can save a lot of storage space. For example, the character class classification library needs to have a higher resolution as the shooting of the portrait is better. Therefore, the pixels of the image in the character class classification library are not compressed, and the file class classification library generally stores the characters, files, and formulas, so the pixels of the image in the file class classification library can be Appropriate amount of compression, such as compression of 70%, due to the reduction of the pixels of the image, can reduce the storage size of the image, thereby saving the storage space of the electronic device.

It can be seen from the above that the embodiment of the present application determines the sample tags corresponding to the image by performing feature analysis on the image in the album, and classifies the image based on the sample tags, moves the image into the corresponding classification library, and according to the preset rule The resolution of the image in the classification library is compressed, and the corresponding compression operation is performed on the pixels of different types of images, which saves the storage space of the electronic device and improves the utilization efficiency of the storage space of the electronic device.

Further, extracting the feature information of the image based on the convolutional neural network can improve the accuracy of the image feature recognition and is more suitable for the user's usage habits.

The classification method of the present application will be further described below based on the method described in the above embodiments. Referring to FIG. 2, the image processing method may include:

301. Acquire an image in an album and extract a plurality of features of the image through a convolutional neural network.

The electronic device analyzes the edge and shape of the image, extracts the overall visual effect, finds different recognition features, and extracts it to find the features in the image that are different from the surrounding environment.

As shown in FIG. 3, an image 101 is displayed on the display screen of the electronic device 100 in the first step 1, and a plurality of features of the image 101 are extracted by a convolutional neural network, such as 1011 and 1012 distinguished from the surrounding environment. .

302. Generate corresponding multiple entity words according to multiple features.

The entity word corresponding to each feature can be obtained by further analyzing the extracted multiple features, and the entity word is used to summarize the feature.

As shown in FIG. 3, in the second step, the electronic device analyzes the

features

1011 and 1012 to determine that the entity word of the feature 1011 is a person, and the entity word of 1012 is a car.

303. Perform embedding processing on multiple features and multiple entity words to generate a corresponding entity description.

The corresponding entity description may be generated according to the correspondence between the multiple features in the image and the plurality of entity words, and the image may be initially recognized by the entity description, as shown in FIG. 3, according to the feature 1011 and the feature 1012. The location relationship, and the entity words corresponding to the feature 1011 and the feature 1012, are embedded, and the corresponding entity description can be "person and car".

304. Perform intelligent reorganization on the entity description corresponding to the image, and generate a reorganized entity description.

In an embodiment, a grammar database may be loaded in the electronic device, the grammar database including rules for combining sentences described by the entities.

The intelligent reorganization of the description in the entity description is performed based on the grammar database, so that the reorganized entity description grammar logic is fluent, so that the sample label of the image can be better summarized, for example, the entity description is “person and car”. Intelligent reorganization, the entity that can be reorganized is described as "people standing next to the car to take pictures"

305. Analyze the reorganized entity description to determine a corresponding sample label.

Among them, by analyzing the reorganized entity description, the keyword is extracted as a sample tag of the image.

For example, if the reorganized entity is described as "people standing next to the car taking pictures" for keyword extraction, the keywords "person", "car" and "photograph" can be extracted.

In an embodiment, a plurality of classification libraries may be established, and each classification library corresponds to a different classification keyword. For example, the character classification library, the classification keywords corresponding to the character classification library are people, faces, eyes, photographs, and the body. And the file type classification library, the classification keywords corresponding to the file type classification library are characters, files, formulas, and the like.

306. Obtain a keyword indicated by the sample label.

The keyword indicated by the sample label of the image is obtained, for example, the keywords for obtaining the sample label are “person”, “car” and “photograph”.

307. Obtain a classification keyword corresponding to the classification library.

The classification keywords corresponding to the classification library are obtained, for example, the classification keywords corresponding to the character classification library are people, faces, eyes, photographs, and bodies, and the classification keywords corresponding to the file type classification library are characters, files, and formulas.

308. Determine whether the keyword matches the classification keyword.

When it is determined that the keyword matches the classification keyword, step 309 is performed; when it is determined that the keyword does not match the classification keyword, the process returns to step 307 to acquire the classification keyword corresponding to the other classification library.

In an embodiment, when it is detected that the keyword indicated by the sample label does not match the classification keyword indicated by all the classification libraries, the prompt information may be generated to prompt the user to perform manual classification processing on the image.

309. Move the image to the classification library.

Wherein, when it is determined that the keyword matches the classification keyword, it indicates that the usage function corresponding to the image meets the requirements of the classification library, and the image is moved into the classification library. It should be particularly noted that the movement in this embodiment is a cutting operation, that is, cutting the storage location of the image into a corresponding storage location in the classification library.

310. Acquire images in the classification library, and generate thumbnail images of all images and display the list.

Among them, in order to avoid erroneously compressing some images, all the images in the classification library are displayed in the form of thumbnails between compression processing operations.

In an embodiment, the thumbnail further includes a check button. When the check button is selected, the image needs to be compressed. When the check button is not selected, the image does not need to be compressed. The default checkmarks on the thumbnails of all images are selected.

311. Generate prompt information, and receive an operation instruction input by the user according to the prompt information.

The prompt information is used to indicate whether the user performs compression processing on the image. At this time, the user can manually cancel the image that does not need to be compressed, for example, the user can manually cancel the selected state on the thumbnail of the image. And when the user confirms, an operation instruction is generated corresponding to the target image that needs to be subjected to compression processing. Based on this, the flexible compression processing of the image can avoid batch processing and also compress some images that do not need to be processed by the pixel, so that the user may be affected by the pixel due to the low pixel.

312. Perform compression processing on a resolution of the target image in the classification library according to a preset rule.

Among them, different compression rules can be set for different classification libraries. For example, the character class classification library requires a higher resolution as the shooting of the portrait, so the compression rule is not compression, and the file class classification library It is generally used to capture text, files, and formulas, so the pixels of the image in the file class classification library can be compressed in an appropriate amount, such as a compression rule of 70%. Based on this, an image that is not particularly high in pixel requirements can be compressed in an appropriate amount, and the storage space occupied by the image can be greatly saved while ensuring that the user is not affected.

In some embodiments, after compressing the resolution of the target image, the user may also recover the resolution before the target image is compressed by the recall operation.

An embodiment of the present invention provides an image processing apparatus, including:

a mobile unit, configured to classify the image based on the sample label, and move the image to a corresponding classification library;

And a processing unit, configured to perform compression processing on the resolution of the image in the classification library according to a preset rule.

In an embodiment, the analyzing unit may include: an extracting subunit, configured to acquire an image in the album, and extract a plurality of features of the image by using a convolutional neural network; and generate a subunit for using the plurality of The feature generates a plurality of corresponding entity words; the embedded subunit is configured to embed the plurality of features and the plurality of entity words to generate a corresponding entity description.

In an embodiment, the label determining unit may include: a recombining subunit, configured to perform intelligent reorganization of the entity description corresponding to the image, generate a reorganized entity description, and determine a subunit for the reorganized entity The description is analyzed to determine the corresponding sample label.

In an embodiment, the mobile unit is specifically configured to: obtain a keyword indicated by the sample tag; acquire a classification keyword corresponding to the classification library; determine whether the keyword matches the classification keyword; and determine the key When the word matches the category keyword, the image is moved to the classification library.

In an embodiment, the mobile unit is further configured to: obtain a keyword indicated by the sample tag; acquire a classification keyword corresponding to the classification library; determine whether the keyword matches the classification keyword; and determine the When the keyword matches the classification keyword, the image is moved into the classification library; when it is determined that the keyword does not match the classification keyword, the prompt information is generated to prompt the user to classify the image; or When it is determined that the keyword does not match the classification keyword, the similarity between the keyword and the classification keyword is obtained, and the image is moved to the classification database with the highest similarity.

An image processing apparatus is also provided in an embodiment. Please refer to FIG. 4. FIG. 4 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present application. Wherein the image processing apparatus is applied to an electronic device, the image processing apparatus comprising an analyzing unit 401, a label determining unit 402, a moving unit 403, and a processing unit 404, as follows:

The analyzing unit 401 is configured to acquire an image in an album, and perform image analysis on the image;

The analyzing unit 401 acquires the features of the image through the convolutional neural network, and then generates corresponding entity words according to the features, such as the convolutional neural network acquiring the features of the characters in the image, and then generating the entity words according to the characteristics of the characters. For people.

Further, the analyzing unit 401 embeds the feature and the entity word acquired through the convolutional neural network into the same space, and then selects the optimal description to generate an entity description. Through the description of the entity, the image can be initially recognized so that the image can be classified later.

The label determining unit 402 is configured to determine a sample label corresponding to the image according to the image analysis result.

The tag determining unit 402 may reorganize the entity description through a certain grammar database to generate a grammatical logic compliant entity description to summarize the sample tags of the image.

Further, the tag determining unit 402 extracts a sample tag in which the keyword determines the image by analyzing the reorganized entity description.

The moving unit 403 is configured to perform classification processing on the image based on the sample label, and move the image into a corresponding classification library.

The mobile unit 403 determines whether the two are matched by respectively acquiring the keyword indicated by the sample tag and the classification keyword corresponding to the classification library, and when the two are matched, the image is moved to the classification library. When it is determined that the two do not match, the classification keyword corresponding to the indication of another classification library is continuously obtained.

The processing unit 404 is configured to perform compression processing on the resolution of the image in the classification library according to a preset rule.

The processing unit 404 compresses the resolution of the images in different classification libraries according to the preset rule, which can save a large amount of storage space. For example, the character classification library needs to be higher because it is for shooting a portrait. The better the resolution, the pixels of the image in the character class classification library are not compressed, and the file class classification library generally stores the characters, files and formulas, so the file class classification library can be used. The pixels of the image are compressed by an appropriate amount, such as 70% compression. Due to the reduction of the pixels of the image, the storage size of the image can be correspondingly reduced, thereby saving the storage space of the electronic device.

In an embodiment, referring to FIG. 5, the analyzing unit 401 may include:

An extracting subunit 4011, configured to acquire an image in the album, and extract a plurality of features of the image by using a convolutional neural network;

a generating subunit 4012, configured to generate a corresponding plurality of entity words according to the plurality of features;

The embedding sub-unit 4013 is configured to perform embedding processing on the plurality of features and the plurality of entity words to generate a corresponding entity description.

In an embodiment, referring to FIG. 5, the label determining unit 402 may include:

The recombination sub-unit 4021 is configured to perform intelligent reorganization on the entity description corresponding to the image to generate a reorganized entity description.

The determining sub-unit 4022 is configured to analyze the reorganized description of the entity to determine a corresponding sample tag.

The steps performed by each unit in the image processing apparatus may refer to the method steps described in the foregoing method embodiments. The image processing device can be integrated in an electronic device such as a mobile phone, a tablet, or the like.

In the specific implementation, the foregoing various units may be implemented as an independent entity, and may be implemented in any combination, and may be implemented as the same entity or a plurality of entities. For the specific implementation of the foregoing units, refer to the foregoing embodiments, and details are not described herein again.

An embodiment of the present application further provides an electronic device. Referring to FIG. 6, the electronic device 500 includes a processor 501 and a memory 502. The processor 501 is electrically connected to the memory 502.

The processor 500 is a control center of the electronic device 500, which connects various parts of the entire electronic device using various interfaces and lines, and executes by running or loading a computer program stored in the memory 502 and calling data stored in the memory 502. The various functions of the electronic device 500 and processing of the data enable overall monitoring of the electronic device 500.

The memory 502 can be used to store software programs and modules, and the processor 501 executes various functional applications and data processing by running computer programs and modules stored in the memory 502. The memory 502 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, a computer program required for at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may be stored according to Data created by the use of electronic devices, etc. Moreover, memory 502 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. Accordingly, memory 502 can also include a memory controller to provide processor 501 access to memory 502.

In the embodiment of the present application, the processor 501 in the electronic device 500 loads the instructions corresponding to the process of one or more computer programs into the memory 502 according to the following steps, and is stored in the memory 502 by the processor 501. The computer program in which to implement various functions, as follows:

Obtain an image in the album and perform image analysis on the image;

In some embodiments, when acquiring an image in an album and performing image analysis on the image, the processor 501 may specifically perform the following steps:

Acquiring an image in the album and extracting a plurality of features of the image through a convolutional neural network;

Generating a corresponding plurality of entity words according to the plurality of features;

The plurality of features and the plurality of entity words are embedded in a process to generate a corresponding entity description.

In some embodiments, when determining the sample tag corresponding to the image according to the image analysis result, the processor 501 may specifically perform the following steps:

Performing intelligent reorganization on the entity description corresponding to the image to generate a reorganized entity description;

The reorganized description of the entity is analyzed to determine the corresponding sample label.

In some embodiments, when the image is classified based on the sample tag, and the image is moved to the corresponding classification library, the processor 501 may specifically perform the following steps:

Obtaining keywords indicated by the sample tag;

Obtaining a classification keyword corresponding to the classification library;

Determining whether the keyword matches the classified keyword;

When it is determined that the keyword matches the classified keyword, the image is moved into the classification library.

In some embodiments, after determining whether the keyword matches the classified keyword, the processor 501 may further perform the following steps:

When it is determined that the keyword does not match the classified keyword, generating prompt information to prompt the user to classify the image; or

When it is determined that the keyword does not match the classification keyword, the similarity between the keyword and the classification keyword is obtained, and the image is moved to the classification database with the highest similarity.

In some embodiments, before compressing the resolution of the image in the classification library according to a preset rule, the processor 501 may further perform the following steps:

Obtain images in the classification library, and generate thumbnail images of all images and display the list;

Generating prompt information, the prompt information is used to indicate whether the user performs compression processing on the image;

Receiving an operation instruction input by the user according to the prompt information, the operation instruction indicating a target image that needs to be subjected to compression processing;

The compression processing of the image in the classification library according to a preset rule includes:

The resolution of the target image in the classification library is compressed according to a preset rule.

In some embodiments, when the resolution of the image in the classification library is compressed according to a preset rule, the processor 501 may specifically perform the following steps:

Referring to FIG. 7 together, in some embodiments, the electronic device 500 may further include: a display 503, a radio frequency circuit 504, an audio circuit 505, and a power source 506. The display 503, the radio frequency circuit 504, the audio circuit 505, and the power source 506 are electrically connected to the processor 501, respectively.

The display 503 can be used to display information entered by a user or information provided to a user, as well as various graphical user interfaces, which can be composed of graphics, text, icons, video, and any combination thereof. The display 503 can include a display panel. In some embodiments, the display panel can be configured in the form of a liquid crystal display (LCD) or an organic light-emitting diode (OLED).

The radio frequency circuit 504 can be used to transmit and receive radio frequency signals to establish wireless communication with network devices or other electronic devices through wireless communication, and to transmit and receive signals with network devices or other electronic devices.

The audio circuit 505 can be used to provide an audio interface between the user and the electronic device through a speaker, a microphone.

The power source 506 can be used to power various components of the electronic device 500. In some embodiments, the power source 506 can be logically coupled to the processor 501 through a power management system to enable functions such as managing charging, discharging, and power management through the power management system.

Although not shown in FIG. 7, the electronic device 500 may further include a camera, a Bluetooth module, and the like, and details are not described herein again.

The embodiment of the present application further provides a storage medium storing a computer program, when the computer program is run on a computer, causing the computer to execute the image processing method in any of the above embodiments, for example, acquiring an image in the album. And performing image analysis on the image; determining a sample label corresponding to the image according to the image analysis result; classifying the image based on the sample label, moving the image to a corresponding classification library; The resolution of the image in the classification library is compressed.

In the embodiment of the present application, the storage medium may be a magnetic disk, an optical disk, a read only memory (ROM), or a random access memory (RAM).

In the above embodiments, the descriptions of the various embodiments are different, and the details that are not detailed in a certain embodiment can be referred to the related descriptions of other embodiments.

It should be noted that, for the image processing method of the embodiment of the present application, a common tester in the art can understand all or part of the process of implementing the image processing method of the embodiment of the present application, which can be completed by controlling the related hardware through a computer program. The computer program may be stored in a computer readable storage medium, such as in a memory of the electronic device, and executed by at least one processor within the electronic device, and may include, for example, an image processing method during execution. The flow of the embodiment. The storage medium may be a magnetic disk, an optical disk, a read only memory, a random access memory, or the like.

For the image processing apparatus of the embodiment of the present application, each functional module may be integrated into one processing chip, or each module may exist physically separately, or two or more modules may be integrated into one module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. The integrated module, if implemented in the form of a software functional module and sold or used as a standalone product, may also be stored in a computer readable storage medium, such as a read only memory, a magnetic disk or an optical disk, etc. .

The image processing method, device, storage medium and electronic device provided by the embodiments of the present application are described in detail. The principles and implementations of the present application are described in the specific examples. The description of the above embodiments is only The method for understanding the present application and its core idea; at the same time, those skilled in the art, according to the idea of the present application, there will be changes in the specific embodiment and the scope of application, in summary, the present specification The content should not be construed as limiting the application.

Claims

An image processing method, comprising:

Acquiring images in the album and performing image analysis on the images;

Determining a sample label corresponding to the image according to an image analysis result;

Performing classification processing on the image based on the sample tag, and moving the image into a corresponding classification library;

The resolution of the image in the classification library is compressed according to a preset rule.
The image processing method according to claim 1, wherein the acquiring an image in the album and performing image analysis on the image comprises:

Acquiring images in the album and extracting a plurality of features of the image through a convolutional neural network;

Generating a corresponding plurality of entity words according to the plurality of features;

The plurality of features and the plurality of entity words are embedded in a process to generate a corresponding entity description.
The image processing method according to claim 2, wherein determining the sample tag corresponding to the image according to the image analysis result comprises:

Performing intelligent reorganization on the entity description corresponding to the image to generate a reorganized entity description;

The reorganized description of the entity is analyzed to determine a corresponding sample tag.
The image processing method according to claim 1, wherein the image is classified based on the sample tag, and the image is moved to a corresponding class library, including:

Obtaining keywords indicated by the sample label;

Obtaining a classification keyword corresponding to the classification library;

Determining whether the keyword matches the classification keyword;

When it is determined that the keyword matches the classification keyword, the image is moved into the classification library.
The image processing method according to claim 4, wherein after determining whether the keyword matches the classification keyword, the method further comprises:

When it is determined that the keyword does not match the classification keyword, generating prompt information to prompt the user to classify the image; or

When it is determined that the keyword does not match the classification keyword, the similarity between the keyword and the classification keyword is obtained, and the image is moved to a classification database with the highest similarity.
The image processing method according to claim 4, wherein before the compression of the resolution of the image in the classification library is performed according to a preset rule, the method further includes:

Obtain images in the classification library, and generate thumbnail images of all images and display the list;

Generating prompt information, the prompt information is used to indicate whether the user performs compression processing on the image;

Receiving an operation instruction input by the user according to the prompt information, the operation instruction indicating a target image that needs to be subjected to compression processing.
The image processing method according to claim 6, wherein the compressing the resolution of the image in the classification library according to a preset rule comprises:

The resolution of the target image in the classification library is compressed according to a preset rule.
An image processing apparatus, comprising:

An analyzing unit, configured to acquire an image in an album, and perform image analysis on the image;

a label determining unit, configured to determine a sample label corresponding to the image according to the image analysis result;

a moving unit, configured to perform classification processing on the image based on the sample label, and move the image into a corresponding classification library;

And a processing unit, configured to perform compression processing on a resolution of the image in the classification library according to a preset rule.
The image processing device according to claim 8, wherein said analyzing unit comprises:

Extracting a subunit for acquiring an image in an album, and extracting a plurality of features of the image by a convolutional neural network;

Generating a subunit, configured to generate a corresponding plurality of entity words according to the plurality of features;

An embedding sub-unit is configured to embed the plurality of features and the plurality of entity words to generate a corresponding entity description.
The image processing device according to claim 9, wherein said label determining unit comprises:

a recombination subunit, configured to perform intelligent reorganization on the entity description corresponding to the image, and generate a reorganized entity description;

Determining a subunit for analyzing the reorganized entity description to determine a corresponding sample tag.
The image processing device according to claim 8, wherein the mobile unit is specifically configured to:

Obtaining keywords indicated by the sample label;

Obtaining a classification keyword corresponding to the classification library;

Determining whether the keyword matches the classification keyword;

When it is determined that the keyword matches the classification keyword, the image is moved into the classification library.
The image processing device according to claim 8, wherein the mobile unit is further configured to:

Obtaining keywords indicated by the sample label;

Obtaining a classification keyword corresponding to the classification library;

Determining whether the keyword matches the classification keyword;

When it is determined that the keyword matches the classification keyword, moving the image into the classification library;

When it is determined that the keyword does not match the classification keyword, generating prompt information to prompt the user to classify the image; or

When it is determined that the keyword does not match the classification keyword, the similarity between the keyword and the classification keyword is obtained, and the image is moved to a classification database with the highest similarity.
A storage medium having stored thereon a computer program, wherein when the computer program is run on a computer, the computer is caused to perform the image processing method according to claim 1.
An electronic device comprising a processor and a memory, the memory having a computer program, wherein the processor is configured to perform the steps by calling the computer program:

Acquiring images in the album and performing image analysis on the images;

Determining a sample label corresponding to the image according to an image analysis result;

Performing classification processing on the image based on the sample tag, and moving the image into a corresponding classification library;

The resolution of the image in the classification library is compressed according to a preset rule.
The electronic device of claim 14 wherein said processor is operative to perform the steps by invoking said computer program:

Acquiring images in the album and extracting a plurality of features of the image through a convolutional neural network;

Generating a corresponding plurality of entity words according to the plurality of features;

The plurality of features and the plurality of entity words are embedded in a process to generate a corresponding entity description.
The electronic device of claim 15 wherein said processor is operative to perform the steps by invoking said computer program:

Performing intelligent reorganization on the entity description corresponding to the image to generate a reorganized entity description;

The reorganized description of the entity is analyzed to determine a corresponding sample tag.
The electronic device of claim 14 wherein said processor is operative to perform the steps by invoking said computer program:

Obtaining keywords indicated by the sample label;

Obtaining a classification keyword corresponding to the classification library;

Determining whether the keyword matches the classification keyword;

When it is determined that the keyword matches the classification keyword, the image is moved into the classification library.
The electronic device of claim 17 wherein said processor is further operative to perform the steps by invoking said computer program:

When it is determined that the keyword does not match the classification keyword, generating prompt information to prompt the user to classify the image; or

When it is determined that the keyword does not match the classification keyword, the similarity between the keyword and the classification keyword is obtained, and the image is moved to a classification database with the highest similarity.
The electronic device of claim 17 wherein said processor is further operative to perform the steps by invoking said computer program:

Obtain images in the classification library, and generate thumbnail images of all images and display the list;

Generating prompt information, the prompt information is used to indicate whether the user performs compression processing on the image;

Receiving an operation instruction input by the user according to the prompt information, the operation instruction indicating a target image that needs to be subjected to compression processing.
The electronic device of claim 19, wherein said processor is operative to perform the steps by invoking said computer program:

The resolution of the target image in the classification library is compressed according to a preset rule.