CN113033680A - Video classification method and device, readable medium and electronic equipment - Google Patents
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Abstract
The disclosure relates to a video classification method, a video classification device, a readable medium and an electronic device, wherein the method comprises the following steps: acquiring a classification label of a target video as a first label; under the condition that the playing times are higher than a first preset playing amount threshold value, determining a predicted label of a target video as a second label through a first video classification model trained in advance; and determining the classification label of the target video as the second label under the condition that the second label is not the same as the first label. Therefore, after the playing frequency is higher than the first preset playing amount threshold, video classification labels of the target videos can be predicted again in a mode different from that of determining the first labels, so that second labels of the target videos are obtained, and then the video classification labels to which the target videos belong are corrected according to the second labels, so that the accuracy of the classification labels of the target videos with higher playing frequency can be guaranteed, and the accuracy of the classification labels of the target videos with higher playing frequency is improved.
Description
Technical Field
The present disclosure relates to the field of video technologies, and in particular, to a video classification method, an apparatus, a readable medium, and an electronic device.
Background
In the prior art, when the labels of the videos in each platform are classified, only one classification mode is usually adopted, for example, a machine learning model trained in advance, however, the types of videos in the current video platform are too many, the contents are too complicated, and only the machine learning model trained in advance is used, so that a good classification effect on various types of videos is difficult to achieve.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
In a first aspect, the present disclosure provides a video classification method, including:
acquiring a classification label of a target video as a first label;
under the condition that the playing times are higher than a first preset playing amount threshold value, determining a predicted label of the target video as a second label through a first video classification model trained in advance;
determining the classification label of the target video as the second label if the second label is not the same label as the first label.
In a second aspect, the present disclosure provides a video classification apparatus, the apparatus comprising:
the acquisition module is used for acquiring a classification label of a target video as a first label;
the determining module is used for determining a predicted label of the target video as a second label through a pre-trained first video classification model under the condition that the playing times is higher than a first preset playing amount threshold;
and the correction module is used for determining the classification label of the target video as the second label under the condition that the second label is not the same as the first label.
In a third aspect, the present disclosure provides a computer readable medium having stored thereon a computer program which, when executed by a processing apparatus, performs the steps of the method of the first aspect.
In a fourth aspect, the present disclosure provides an electronic device comprising:
a storage device having a computer program stored thereon;
processing means for executing the computer program in the storage means to carry out the steps of the method of the first aspect.
Through the technical scheme, the video classification label can be determined for the video without the playing frequency information or the video with the less playing frequency in any video classification mode, for example, the mode of the second video classification model, so that the corresponding video label can be obtained for any video to serve as the first label; after the playing frequency is higher than the first preset playing amount threshold, the video classification label of the target video can be predicted again in a different manner from that of determining the first label, for example, the second label of the target video is obtained by determining through the first video classification model, and then the video classification label to which the target video belongs is corrected according to the second label, so that the accuracy of the classification label of the target video with higher playing frequency can be ensured.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale. In the drawings:
fig. 1 is a flowchart illustrating a video classification method according to an exemplary embodiment of the present disclosure.
Fig. 2 is a flowchart illustrating a video classification method according to still another exemplary embodiment of the present disclosure.
Fig. 3 is a flowchart illustrating a method of training the first video classification model in a video classification method according to yet another exemplary embodiment of the present disclosure.
Fig. 4 is a block diagram illustrating a structure of a video classification apparatus according to an exemplary embodiment of the present disclosure.
FIG. 5 illustrates a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Fig. 1 is a flowchart illustrating a video classification method according to an exemplary embodiment of the present disclosure. As shown in fig. 1, the method includes steps 101 to 103.
In step 101, a classification tag of a target video is acquired as a first tag. The target video may be any form of video that requires tag classification. For example, the short video published by the user in the short video platform may be used, or the long video published by the user in other video platforms may be used.
In step 102, in a case that the playing times are higher than a first preset playing amount threshold, determining a predicted label of the target video as a second label through a first video classification model trained in advance.
The playing times can be determined according to the time recording rules of different video platforms, and the specific determination method of the playing times is not limited in the application.
When the playing time is higher than the first preset playing amount threshold, it may be characterized that the playing time of the target video is higher, that is, the first preset playing amount threshold may be used to screen out the target video with higher playing time as the video for predicting the second tag.
The first video classification model for determining the second label may be any video classification model as long as the prediction label of the target video is obtained.
In step 103, in the case that the second label is not the same label as the first label, the classification label of the target video is determined as the second label.
And performing secondary label prediction on the target video with higher playing times, thereby improving the label accuracy of the target video with higher playing times.
The classification label as the first label of the target video may be a classification label determined by any means. For example, by the method shown in fig. 2.
Fig. 2 is a flowchart illustrating a video classification method according to still another exemplary embodiment of the present disclosure. As shown in fig. 2, the method further includes step 201 and step 202.
In step 201, determining the classification label of the target video through a second video classification model;
in step 202, the classification label of the target video is taken as a first label.
The second video classification model and the first video classification model are different video classification models. Different video classification models obtained by training different training samples of the models, different video classification models obtained by different adopted video classification methods, different video classification models obtained by different adopted neural networks, and the like can be obtained as long as the different video classification models are not the same as the first video classification model.
In one possible embodiment, the training data of the second video classification model may be sample videos with play times and/or without play times. That is, the training data of the second video classification model is random labeling data.
Through the technical scheme, the video classification label can be determined for the video without the playing frequency information or the video with the less playing frequency in any video classification mode, for example, the mode of the second video classification model, so that the corresponding video label can be obtained for any video to serve as the first label; after the playing frequency is higher than the first preset playing amount threshold, the video classification label of the target video can be predicted again in a different manner from that of determining the first label, for example, the second label of the target video is obtained by determining through the first video classification model, and then the video classification label to which the target video belongs is corrected according to the second label, so that the accuracy of the classification label of the target video with higher playing frequency can be ensured.
In another possible implementation manner, the training data of the first video classification model is a sample video with a playing time higher than a second preset playing amount threshold. That is, when the first video classification model is trained, all the adopted training video data are sample videos with high playing amount. In this way, the effect of the machine learning model is influenced by the data distribution, and the machine learning model can only be learned on a certain fixed distribution, so the machine learning model is most effective on a distribution similar to the training set. Therefore, the first video classification model can have a better effect on the label prediction of the target video with the playing times higher than the second preset playing amount threshold value by limiting the playing time distribution of the training data of the first video classification model.
For example, the applicant finds that in most short video platforms, if a plurality of short videos uploaded for a fixed duration are played at different playing times, the distribution of the video contents of the short videos is greatly different, for example, in videos with lower playing times, the video contents are single and the information content is not high along with the fact that the video is shot at a high ratio, and in videos with higher playing times, the content occupation ratio of drama with higher information content, knowledge science popularization and the like is remarkably improved. There is a certain difference in video content between different play times distributions. Therefore, if the threshold of the training data for training the first video classification model is defined according to the playing times, the classification prediction performance of the first video classification model obtained through training in the target video in the distribution of which the playing times are higher than the first preset playing amount threshold can be better, and the accuracy is higher.
The first preset playing amount threshold and the second preset playing amount threshold may be set to be completely the same according to practical applications, or two similar different thresholds, as long as the effect of the first video classification model on the classification and prediction of the tags of the target video with the interference higher than the first preset playing amount threshold is achieved, which is better than the effect of predicting the classification of the first tag of the target video. For example, the second preset playing amount threshold may be set to be relatively lower than the first preset playing amount threshold, and it is sufficient to ensure that the playing times higher than the first preset playing amount threshold are all included in the playing times higher than the second preset playing amount threshold, and the specific value is set without limitation in this disclosure.
Fig. 3 is a flowchart illustrating a method of training the first video classification model in a video classification method according to yet another exemplary embodiment of the present disclosure. As shown in fig. 3, the method includes steps 301 to 303.
In step 301, a sample video with the playing time higher than the second preset playing amount threshold is obtained. The playing times can be determined according to the playing time recording rules of different video platforms.
In step 302, class label labeling is performed on the sample video. The manner of labeling the sample video with the classification label can be manually labeled. When the first label of the target video is determined by the second video classification model, for example, the classification label of each sample video in the training data of the second video classification model may be manually labeled.
In step 303, model training is performed on the first video classification model through the sample video labeled with the classification label.
Fig. 4 is a block diagram illustrating a structure of a video classification apparatus according to an exemplary embodiment of the present disclosure. As shown in fig. 4, the apparatus includes: an obtaining module 10, configured to obtain a classification tag of a target video as a first tag; a determining module 20, configured to determine, through a first video classification model trained in advance, a predicted tag of the target video as a second tag when the playing time is higher than a first preset playing amount threshold; a modification module 30, configured to determine the classification label of the target video as the second label if the second label is not the same as the first label.
Through the technical scheme, the video classification label can be determined for the video without the playing frequency information or the video with the less playing frequency in any video classification mode, for example, the mode of the second video classification model, so that the corresponding video label can be obtained for any video to serve as the first label; after the playing frequency is higher than the first preset playing amount threshold, the video classification label of the target video can be predicted again in a different manner from that of determining the first label, for example, the second label of the target video is obtained by determining through the first video classification model, and then the video classification label to which the target video belongs is corrected according to the second label, so that the accuracy of the classification label of the target video with higher playing frequency can be ensured.
In a possible implementation manner, the training data of the first video classification model is a sample video with a playing time higher than a second preset playing amount threshold.
In one possible embodiment, the first video classification model is trained by: acquiring a sample video with the playing times higher than the second preset playing amount threshold; performing classification label labeling on the sample video; and performing model training on the first video classification model through the sample video marked with the classification label.
In a possible implementation, the acquisition module 10 comprises: the first obtaining submodule is used for determining the classification label of the target video through a second video classification model; and the second obtaining submodule is used for taking the classification label of the target video as the first label.
In a possible embodiment, the training data of the second video classification model is a sample video with and/or without play times.
Referring now to FIG. 5, a block diagram of an electronic device 500 suitable for use in implementing embodiments of the present disclosure is shown. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 5 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. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 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 carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program performs the above-described functions defined in the methods of the embodiments of the present disclosure when executed by the processing device 501.
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.
In some embodiments, the communication may be performed using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
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 a classification label of a target video as a first label; under the condition that the playing times are higher than a first preset playing amount threshold value, determining a predicted label of the target video as a second label through a first video classification model trained in advance; determining the classification label of the target video as the second label if the second label is not the same label as the first label.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to 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 modules described in the embodiments of the present disclosure may be implemented by software or hardware. The name of the module does not in some cases constitute a limitation of the module itself, and for example, the acquiring module may also be described as a "module that acquires a classification tag of a target video as a first tag".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.
Example 1 provides a video classification method according to one or more embodiments of the present disclosure, the method including: acquiring a classification label of a target video as a first label; under the condition that the playing times are higher than a first preset playing amount threshold value, determining a predicted label of the target video as a second label through a first video classification model trained in advance; determining the classification label of the target video as the second label if the second label is not the same label as the first label.
Example 2 provides the method of example 1, and the training data of the first video classification model is a sample video with a play time higher than a second preset play amount threshold.
Example 3 provides the method of example 2, the first video classification model being trained by: acquiring a sample video with the playing times higher than the second preset playing amount threshold; performing classification label labeling on the sample video; and performing model training on the first video classification model through the sample video marked with the classification label.
Example 4 provides the method of example 1, wherein the obtaining the classification label of the target video as the first label includes: determining the classification label of the target video through a second video classification model; and taking the classification label of the target video as a first label.
Example 5 provides the method of example 4, wherein the training data of the second video classification model is sample video with and/or without play times.
Example 6 provides, in accordance with one or more embodiments of the present disclosure, a video classification apparatus, the apparatus comprising: the acquisition module is used for acquiring a classification label of a target video as a first label; the determining module is used for determining a predicted label of the target video as a second label through a pre-trained first video classification model under the condition that the playing times is higher than a first preset playing amount threshold; and the correction module is used for determining the classification label of the target video as the second label under the condition that the second label is not the same as the first label.
Example 7 provides the apparatus of example 6, in accordance with one or more embodiments of the present disclosure, wherein the training data of the first video classification model is a sample video with a play count higher than a second preset play amount threshold.
Example 8 provides the apparatus of example 7, the first video classification model being trained by: acquiring a sample video with the playing times higher than the second preset playing amount threshold; performing classification label labeling on the sample video; and performing model training on the first video classification model through the sample video marked with the classification label.
Example 9 provides a computer readable medium having stored thereon a computer program that, when executed by a processing apparatus, performs the steps of the method of any of examples 1-5, in accordance with one or more embodiments of the present disclosure.
Example 10 provides, in accordance with one or more embodiments of the present disclosure, an electronic device comprising: a storage device having a computer program stored thereon; processing means for executing the computer program in the storage means to carry out the steps of the method of any of examples 1-5.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
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