CN111753114A - Image pre-labeling method and device and electronic equipment - Google Patents
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Abstract
The embodiment of the disclosure provides an image pre-labeling method, an image pre-labeling device and electronic equipment, belonging to the technical field of data processing, wherein the method comprises the following steps: acquiring a plurality of original images which do not contain the classification labels to form an image set; performing similarity transformation on each original image in the image set to obtain a transformed image corresponding to the original image; taking an original image and a transformed image corresponding to the original image as positive samples, taking original images with different contents as negative samples, and carrying out feature training on a preset neural network model; and based on the output characteristics extracted from the to-be-labeled image set by the trained neural network model, performing clustering operation on the images in the to-be-labeled image set, so that the clustered images under each cluster are provided with classification labels. By the processing scheme, the efficiency of image classification and labeling can be improved.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to an image pre-labeling method and apparatus, and an electronic device.
Background
Image classification refers to an image processing method for distinguishing objects of different categories from each other based on different features each reflected in image information. It uses computer to make quantitative analysis of image, and classifies each picture element or region in the image into one of several categories to replace human visual interpretation.
The conventional image classification method classifies images by utilizing bottom layer characteristics of the images, such as gray scale, color, texture, shape, position and the like; for example, classifying the image by utilizing the gray histogram feature; classifying the images by using the texture features; classifying the images by adopting texture, edge and color histogram mixed features; adopting an SVM as a classifier; the image is represented by a matrix whose elements are the gray values of the corresponding pixels, and then the image features are extracted by SVD and PCA methods.
The conventional image labeling method usually needs to perform a large amount of data operation, which results in large consumption of system resources.
Disclosure of Invention
In view of the above, embodiments of the present disclosure provide an image pre-labeling method, an image pre-labeling device, and an electronic device, so as to at least partially solve the problems in the prior art.
In a first aspect, an embodiment of the present disclosure provides an image pre-annotation method, including:
acquiring a plurality of original images without classification labels to form an image set, wherein the content of each original image in the image set is different from that of other original images;
performing similarity transformation on each original image in the image set to obtain a transformed image corresponding to the original image;
taking an original image and a transformed image corresponding to the original image as positive samples, taking original images with different contents as negative samples, and performing feature training on a preset neural network model so that the feature distance between the positive samples is smaller than a first feature distance, the feature distance between the negative samples is larger than a second feature distance, and the first feature distance is smaller than the second feature distance;
and based on the output characteristics extracted from the to-be-labeled image set by the trained neural network model, performing clustering operation on the images in the to-be-labeled image set, so that the clustered images under each cluster are provided with classification labels.
According to a specific implementation manner of the embodiment of the present disclosure, the acquiring a plurality of original images not including a classification tag includes:
carrying out similarity judgment on the obtained original image;
selecting pictures with similarity greater than a preset similarity value based on the judgment result to form a similarity image set;
and deleting the original pictures in the similarity set from the image set.
According to a specific implementation manner of the embodiment of the present disclosure, the acquiring a plurality of original images not including a classification tag includes:
presetting a plurality of classifications;
filling a plurality of original images in a plurality of classifications respectively to enable the number of the original images in different classifications to meet the requirement of balance;
and taking a set of original images in a plurality of classifications meeting the balance requirement as the image set.
According to a specific implementation manner of the embodiment of the present disclosure, performing similarity transformation on each original image in the image set to obtain a transformed image corresponding to the original image includes:
performing at least one transformation operation of color change, image cutting, image rotation and color channel extraction on the original image;
and taking the original image after the transformation operation as a transformed image corresponding to the original image.
According to a specific implementation manner of the embodiment of the present disclosure, performing similarity transformation on each original image in the image set to obtain a transformed image corresponding to the original image includes:
carrying out similarity transformation on the original image by utilizing a preset similarity transformation model;
and taking the transformed original image as the transformed image.
According to a specific implementation manner of the embodiment of the present disclosure, the performing feature training on a preset neural network model by using an original image and a transformed image corresponding to the original image as positive samples and using original images with different contents as negative samples includes:
inputting the positive samples into the neural network model;
judging whether the characteristic distance of the positive sample in the neural network model is smaller than the first characteristic distance;
and if so, stopping training the neural network model.
According to a specific implementation manner of the embodiment of the present disclosure, the performing feature training on a preset neural network model by using an original image and a transformed image corresponding to the original image as positive samples and using original images with different contents as negative samples includes:
inputting the negative examples into the neural network model;
judging whether the characteristic distance of the negative sample in the neural network model is larger than the second characteristic distance;
and if so, stopping training the neural network model.
According to a specific implementation manner of the embodiment of the present disclosure, the performing clustering operation on the images in the to-be-labeled image set based on the output features extracted from the trained neural network model to the to-be-labeled image set includes:
performing feature extraction on the images in the image set to be labeled by using the trained neural network model to obtain output features corresponding to the image set to be labeled;
based on the output characteristics, clustering operation is carried out on the images in the image set to be labeled in an unsupervised clustering mode to obtain a plurality of clustering clusters;
and setting different classification names for the plurality of clustering clusters so as to set a classification label of each image in the image set to be labeled based on the classification names.
In a second aspect, an embodiment of the present disclosure provides an image pre-labeling apparatus, including:
the system comprises an acquisition module, a classification module and a classification module, wherein the acquisition module is used for acquiring a plurality of original images which do not contain classification labels to form an image set, and the content of each original image in the image set is different from that of other original images;
the transformation module is used for carrying out similar transformation on each original image in the image set to obtain a transformed image corresponding to the original image;
the training module is used for performing feature training on a preset neural network model by taking an original image and a transformed image corresponding to the original image as positive samples and taking original images with different contents as negative samples, so that the feature distance between the positive samples is smaller than a first feature distance, the feature distance between the negative samples is larger than a second feature distance, and the first feature distance is smaller than the second feature distance;
and the execution module is used for performing clustering operation on the images in the image set to be labeled based on the output characteristics extracted from the trained neural network model to the image set to be labeled, so that the clustered images under each cluster are provided with classification labels.
In a third aspect, an embodiment of the present disclosure further provides an electronic device, where the electronic device includes:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the image pre-labeling method of the first aspect or any implementation manner of the first aspect.
In a fourth aspect, the disclosed embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the image pre-labeling method in the first aspect or any implementation manner of the first aspect.
In a fifth aspect, the disclosed embodiments also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform the image pre-labeling method of the first aspect or any implementation manner of the first aspect.
The image pre-labeling scheme in the embodiment of the disclosure comprises the steps of obtaining a plurality of original images without classification labels to form an image set, wherein the content of each original image in the image set is different from that of other original images; performing similarity transformation on each original image in the image set to obtain a transformed image corresponding to the original image; taking an original image and a transformed image corresponding to the original image as positive samples, taking original images with different contents as negative samples, and performing feature training on a preset neural network model so that the feature distance between the positive samples is smaller than a first feature distance, the feature distance between the negative samples is larger than a second feature distance, and the first feature distance is smaller than the second feature distance; and based on the output characteristics extracted from the to-be-labeled image set by the trained neural network model, performing clustering operation on the images in the to-be-labeled image set, so that the clustered images under each cluster are provided with classification labels. By the processing scheme, the image pre-labeling efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of an image pre-labeling method according to an embodiment of the present disclosure;
FIG. 2 is a flowchart of another image pre-labeling method provided in the embodiments of the present disclosure;
FIG. 3 is a flowchart of another image pre-labeling method provided in the embodiments of the present disclosure;
FIG. 4 is a flowchart of another image pre-labeling method provided in the embodiments of the present disclosure;
fig. 5 is a schematic structural diagram of an image pre-labeling apparatus according to an embodiment of the present disclosure;
fig. 6 is a schematic diagram of an electronic device provided in an embodiment of the present disclosure.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present disclosure, and the drawings only show the components related to the present disclosure rather than the number, shape and size of the components in actual implementation, and the type, amount and ratio of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
The embodiment of the disclosure provides an image pre-labeling method. The image pre-labeling method provided by the embodiment can be executed by a computing device, the computing device can be implemented as software, or implemented as a combination of software and hardware, and the computing device can be integrally arranged in a server, a client and the like.
Referring to fig. 1, an image pre-labeling method in an embodiment of the present disclosure may include the following steps:
s101, acquiring a plurality of original images without classification labels to form an image set, wherein the content of each original image in the image set is different from that of other original images.
Before labeling the pre-labeled image, a neural network model needs to be trained, image features are extracted through the neural network model, and before feature extraction, training data needs to be selected, and through the training data, the neural network model can be trained, and the neural network model can be any form of neural network module, such as a CNN convolutional neural network model.
The sum of all the training data forms an image set, each component in the image set is an original image, and in order to ensure the diversity of the image set, the original images in the image set can be set to be different.
For this reason, in the process of acquiring the training data, a plurality of classifications may be set for the image set, and the acquisition process of the image set is completed by filling data into the plurality of classifications. In the process of acquiring the data, whether the acquired data contains the classification label can be further judged, if the acquired data contains the element classification label, the existing classification label is not adopted, and thus the randomness of the original image can be ensured.
S102, performing similarity transformation on each original image in the image set to obtain a transformed image corresponding to the original image.
After the training data in the image set is obtained, the original image can be further subjected to change processing, and a transformed image corresponding to the original image can be obtained through the transformation processing. The original image may be subjected to feature transformation in various ways as long as the satisfied similarity between the transformed elements and the transformed elements is ensured.
Taking an original image of an image type as an example, the same image can be subjected to various color changes, random cutting, rotation and color channel extraction to obtain images with different characteristics of one image. Of course, other similar arrangements may be used for other types of original images.
S103, taking the original image and the transformed image corresponding to the original image as positive samples and the original image with different contents as negative samples, and performing feature training on a preset neural network model to enable the feature distance between the positive samples to be smaller than a first feature distance, the feature distance between the negative samples to be larger than a second feature distance, and the first feature distance to be smaller than the second feature distance.
After the feature transformation is performed on the original image, the original image and the transformed image corresponding to the original image may be set as a positive sample pair, and a plurality of positive sample pairs are collected together to form a positive sample set. For original images with different contents, a negative sample set may be set. By the arrangement mode, the samples in the positive sample set can meet certain similarity, and the samples in the negative sample set meet certain difference.
The neural network model may be trained by inputting positive and negative examples into the neural network model. In the training process, corresponding training indexes can be set for the neural network model, and whether the trained neural network model meets requirements or not can be judged through the training indexes. As one way, a first feature distance and a second feature distance may be set, and the first feature distance and the second feature distance may be used to represent a degree of similarity between two objects, for example, the first feature distance and the second feature distance may be obtained by using an euclidean distance calculation.
The first characteristic distance may be a relatively small value, the second characteristic distance may be greater than the first characteristic distance, and the second characteristic distance may be a relatively large value. Through the setting mode, the preset neural network model can be subjected to feature training, so that the feature distance between the positive samples is smaller than the first feature distance, and the feature distance between the negative samples is larger than the second feature distance, and the requirement of the neural network model training is met.
And S104, based on the output characteristics extracted from the to-be-labeled image set by the trained neural network model, performing clustering operation on the images in the to-be-labeled image set, so that the clustered images under each cluster are provided with classification labels.
After the neural network model finishes training, the characteristics output by the trained neural network model have higher accuracy and identification degree due to the training of the positive and negative samples. Therefore, the pre-labeling operation of classifying the images in the image set needing to be labeled can be carried out.
Specifically, the trained neural network model may be used to perform feature extraction on the images in the image set to be labeled, so as to obtain output features corresponding to the image set to be labeled. Through the obtained output characteristics, clustering operation can be performed on the images in the image set to be labeled in an unsupervised clustering mode to obtain a plurality of clustering clusters, and different classification names are set for the clustering clusters so as to set the classification label of each image in the image set to be labeled based on the classification names.
Through the content in the embodiment, the pre-labeling operation can be quickly and effectively executed on the image needing to be classified, and the image labeling efficiency is improved.
Referring to fig. 2, according to a specific implementation manner of the embodiment of the present disclosure, the acquiring a plurality of original images not including a classification label includes:
and S201, performing similarity judgment on the acquired original image.
Similarity judgment can be carried out on all the obtained original images by setting a similarity calculation method, and then the similarity value between any images is obtained.
S202, based on the judgment result, selecting the pictures with the similarity greater than the preset similarity value to form a similarity image set.
For the pictures with higher similarity values, the diversity of the images in the image set can be influenced, and therefore, the images with the similarity smaller than the preset value can be further selected by setting the similarity values and comparing the similarity judgment result with the preset value to form the similarity image set.
S203, deleting the original pictures in the similarity set from the image set.
By the embodiment, the diversity of the images in the acquired image set can be ensured.
According to a specific implementation manner of the embodiment of the present disclosure, the acquiring a plurality of original images not including a classification tag includes:
first, a plurality of classifications are set in advance.
By setting a plurality of classifications, a plurality of training data of different classes can be set purposefully, thereby improving the diversity of images in an image set
Secondly, filling a plurality of original images in a plurality of classifications respectively, so that the number of the original images in different classifications meets the requirement of balance.
A certain number of original images can be set in a plurality of classifications, thereby ensuring that the data of the original images in different classifications meet the requirement of equilibrium
And finally, taking the set of original images in a plurality of classifications meeting the balance requirement as the image set.
By the scheme of the embodiment, the obtained original image can meet the requirements of balance and diversity.
According to a specific implementation manner of the embodiment of the present disclosure, performing similarity transformation on each original image in the image set to obtain a transformed image corresponding to the original image includes: performing at least one transformation operation of color change, image cutting, image rotation and color channel extraction on the original image; and taking the original image after the transformation operation as a transformed image corresponding to the original image.
According to a specific implementation manner of the embodiment of the present disclosure, performing similarity transformation on each original image in the image set to obtain a transformed image corresponding to the original image includes: carrying out similarity transformation on the original image by utilizing a preset similarity transformation model; and taking the transformed original image as the transformed image.
According to a specific implementation manner of the embodiment of the present disclosure, the performing feature training on a preset neural network model by using an original image and a transformed image corresponding to the original image as positive samples and using original images with different contents as negative samples includes: inputting the positive samples into the neural network model; judging whether the characteristic distance of the positive sample in the neural network model is smaller than the first characteristic distance; and if so, stopping training the neural network model.
Referring to fig. 3, according to a specific implementation manner of the embodiment of the present disclosure, the performing feature training on a preset neural network model by using an original image and a transformed image corresponding to the original image as positive samples and using original images with different contents as negative samples includes:
s301, inputting the negative sample into the neural network model.
S302, judging whether the characteristic distance of the negative sample in the neural network model is larger than the second characteristic distance.
Whether the positive sample meets the performance index after the neural network model is trained can be judged by setting the second characteristic distance. The second characteristic distance may be set and calculated in a preset manner, for example, the second characteristic distance may be calculated in a euclidean distance manner
And S303, if so, stopping training the neural network model.
By the embodiment, the neural network model can be set and calculated through the second characteristic distance
Referring to fig. 4, according to a specific implementation manner of the embodiment of the present disclosure, the performing clustering operation on the images in the to-be-labeled image set based on the output features extracted from the trained neural network model to the to-be-labeled image set includes:
s401, performing feature extraction on the images in the image set to be labeled by using the trained neural network model to obtain output features corresponding to the image set to be labeled;
s402, based on the output characteristics, clustering operation is carried out on the images in the image set to be labeled in an unsupervised clustering mode to obtain a plurality of clustering clusters;
s403, setting different classification names for the plurality of clustering clusters so as to set a classification label of each image in the image set to be labeled based on the classification names.
Through the content of the embodiment, the pre-labeling operation can be performed on the image set to be labeled based on the trained neural network model, so that the efficiency of labeling work is improved.
Corresponding to the above method embodiment, referring to fig. 5, the embodiment of the present disclosure further provides an image pre-labeling apparatus 50, including:
an obtaining module 501, configured to obtain a plurality of original images that do not include a classification tag, and form an image set, where content of each original image in the image set is different from that of other original images;
a transformation module 502, configured to perform similar transformation on each original image in the image set to obtain a transformed image corresponding to the original image;
a training module 503, configured to perform feature training on a preset neural network model by using an original image and a transformed image corresponding to the original image as positive samples and using original images with different contents as negative samples, so that a feature distance between the positive samples is smaller than a first feature distance, a feature distance between the negative samples is larger than a second feature distance, and the first feature distance is smaller than the second feature distance;
the executing module 504 is configured to perform clustering operation on the images in the to-be-labeled image set based on the output features extracted from the trained neural network model to the to-be-labeled image set, so that the clustered images in each cluster are provided with a classification label.
For parts not described in detail in this embodiment, reference is made to the contents described in the above method embodiments, which are not described again here.
Referring to fig. 6, an embodiment of the present disclosure also provides an electronic device 60, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the image pre-labeling method of the foregoing method embodiments.
The disclosed embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the image pre-labeling method in the aforementioned method embodiments.
The disclosed embodiments also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the image pre-labeling method in the aforementioned method embodiments.
Referring now to FIG. 6, a schematic diagram of an electronic device 60 suitable for use in implementing embodiments of the present disclosure is shown. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic 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 disclosure.
As shown in fig. 6, the electronic device 60 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 60 are also stored. The processing device 601, the ROM602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 60 to communicate with other devices wirelessly or by wire to exchange data. While the figures illustrate an electronic device 60 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure 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 in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or may be installed from the storage means 608, or may be installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, 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 disclosure, 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 contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many 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: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising the at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects the internet protocol addresses from the at least two internet protocol addresses and returns the internet protocol addresses; receiving an internet protocol address returned by the node evaluation equipment; wherein the obtained internet protocol address indicates an edge node in the content distribution network.
Alternatively, the computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from the at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
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 disclosure. 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 and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of a unit does not in some cases constitute a limitation of the unit itself, for example, the first retrieving unit may also be described as a "unit for retrieving at least two internet protocol addresses".
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.
The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present disclosure should be covered within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (11)
1. An image pre-labeling method is characterized by comprising the following steps:
acquiring a plurality of original images without classification labels to form an image set, wherein the content of each original image in the image set is different from that of other original images;
performing similarity transformation on each original image in the image set to obtain a transformed image corresponding to the original image;
taking an original image and a transformed image corresponding to the original image as positive samples, taking original images with different contents as negative samples, and performing feature training on a preset neural network model so that the feature distance between the positive samples is smaller than a first feature distance, the feature distance between the negative samples is larger than a second feature distance, and the first feature distance is smaller than the second feature distance;
and based on the output characteristics extracted from the to-be-labeled image set by the trained neural network model, performing clustering operation on the images in the to-be-labeled image set, so that the clustered images under each cluster are provided with classification labels.
2. The method of claim 1, wherein obtaining a plurality of raw images that do not contain a classification tag comprises:
carrying out similarity judgment on the obtained original image;
selecting pictures with similarity greater than a preset similarity value based on the judgment result to form a similarity image set;
and deleting the original pictures in the similarity set from the image set.
3. The method of claim 1, wherein obtaining a plurality of raw images that do not contain a classification tag comprises:
presetting a plurality of classifications;
filling a plurality of original images in a plurality of classifications respectively to enable the number of the original images in different classifications to meet the requirement of balance;
and taking a set of original images in a plurality of classifications meeting the balance requirement as the image set.
4. The method of claim 1, wherein performing a similarity transformation on each original image in the image set to obtain a transformed image corresponding to the original image comprises:
performing at least one transformation operation of color change, image cutting, image rotation and color channel extraction on the original image;
and taking the original image after the transformation operation as a transformed image corresponding to the original image.
5. The method of claim 1, wherein performing a similarity transformation on each original image in the image set to obtain a transformed image corresponding to the original image comprises:
carrying out similarity transformation on the original image by utilizing a preset similarity transformation model;
and taking the transformed original image as the transformed image.
6. The method according to claim 1, wherein the feature training of the preset neural network model by using the original image and the transformed image corresponding to the original image as positive samples and the original image with different contents as negative samples comprises:
inputting the positive samples into the neural network model;
judging whether the characteristic distance of the positive sample in the neural network model is smaller than the first characteristic distance;
and if so, stopping training the neural network model.
7. The method according to claim 1, wherein the feature training of the preset neural network model by using the original image and the transformed image corresponding to the original image as positive samples and the original image with different contents as negative samples comprises:
inputting the negative examples into the neural network model;
judging whether the characteristic distance of the negative sample in the neural network model is larger than the second characteristic distance;
and if so, stopping training the neural network model.
8. The method according to claim 1, wherein the clustering operation is performed on the images in the image set to be labeled based on the output features extracted from the trained neural network model, and the clustering operation comprises:
performing feature extraction on the images in the image set to be labeled by using the trained neural network model to obtain output features corresponding to the image set to be labeled;
based on the output characteristics, clustering operation is carried out on the images in the image set to be labeled in an unsupervised clustering mode to obtain a plurality of clustering clusters;
and setting different classification names for the plurality of clustering clusters so as to set a classification label of each image in the image set to be labeled based on the classification names.
9. An image pre-labeling apparatus, comprising:
the system comprises an acquisition module, a classification module and a classification module, wherein the acquisition module is used for acquiring a plurality of original images which do not contain classification labels to form an image set, and the content of each original image in the image set is different from that of other original images;
the transformation module is used for carrying out similar transformation on each original image in the image set to obtain a transformed image corresponding to the original image;
the training module is used for performing feature training on a preset neural network model by taking an original image and a transformed image corresponding to the original image as positive samples and taking original images with different contents as negative samples, so that the feature distance between the positive samples is smaller than a first feature distance, the feature distance between the negative samples is larger than a second feature distance, and the first feature distance is smaller than the second feature distance;
and the execution module is used for performing clustering operation on the images in the image set to be labeled based on the output characteristics extracted from the trained neural network model to the image set to be labeled, so that the clustered images under each cluster are provided with classification labels.
10. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the image pre-annotation method of any one of the preceding claims 1-8.
11. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the image pre-annotation method of any of the preceding claims 1-8.
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