CN113033707B - 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 device, a readable medium and an electronic apparatus, comprising: acquiring a target video; respectively inputting the target video into a plurality of video classification models, and determining a plurality of groups of classification prediction results corresponding to the target video; determining target video characteristics of the target video according to the multiple groups of classification prediction results; and inputting the target video characteristics into a pre-trained fusion classification model, and determining classification labels corresponding to the target video. Through the technical scheme, the classification prediction results of the multiple video classification models can be finally fused to serve as the video features of the target video, and then the video classification is identified according to the newly obtained video features, so that the richer classification information of the target video can be obtained, the classification effect of the video classification task is improved, and the classification accuracy of the video is improved.
Description
Technical Field
The disclosure relates to the technical field of videos, and in particular relates to a video classification method, a device, a readable medium and electronic equipment.
Background
Video tag identification and classification are fundamental technologies of video content platforms, and have many applications for detecting and analyzing content in the platform, recommending and searching the content of the platform, and the like. At present, the identification of the video tag is mainly based on a supervised machine learning method, automatic learning is carried out from the annotation data in an end-to-end mode, and then the learned model is automatically predicted on new text data. However, the independent machine learning model has limited capability for extracting video features, and cannot achieve more accurate classification effects.
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, the method comprising:
acquiring a target video;
respectively inputting the target video into a plurality of video classification models, and determining a plurality of groups of classification prediction results corresponding to the target video;
determining target video characteristics of the target video according to the multiple groups of classification prediction results;
and inputting the target video features into a pre-trained fusion classification model, and determining classification labels corresponding to the target videos.
In a second aspect, the present disclosure provides a video classification apparatus, the apparatus comprising:
the first acquisition module is used for acquiring a target video;
the first determining module is used for respectively inputting the target video into a plurality of video classification models and determining a plurality of groups of classification prediction results corresponding to the target video;
the second determining module is used for determining target video characteristics of the target video according to the multiple groups of classification prediction results;
and the third determining module is used for inputting the target video characteristics into a pre-trained fusion classification model and determining classification labels corresponding to the target videos.
In a third aspect, the present disclosure provides a computer readable medium having stored thereon a computer program which when executed by a processing device 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 said computer program in said storage means to carry out the steps of the method of the first aspect.
According to the technical scheme, the classification prediction results corresponding to the target video can be obtained through the plurality of video classification models respectively and used as the classification feature data of the target video, the classification prediction results of the plurality of video classification models are finally fused to be used as the video features of the target video, and then the video classification is identified according to the newly obtained video features, so that the richer classification information of the target video can be obtained, the classification effect of a video classification task is improved, and the classification accuracy of the video is improved.
Additional features and advantages of the present disclosure will be set forth in the detailed description which follows.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components 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 training method of a fusion classification model in a video classification method according to still another exemplary embodiment of the present disclosure.
Fig. 4 is a block diagram illustrating a video classification apparatus according to an exemplary embodiment of the present disclosure.
Fig. 5 is a block diagram illustrating a structure of a video classification apparatus according to still another exemplary embodiment of the present disclosure.
Fig. 6 shows a schematic structural 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 have been shown in the accompanying 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 are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present 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. Furthermore, 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 "including" and variations thereof as used herein are intended to be 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. Related definitions of other terms will be given in the description below.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such 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 104.
In step 101, a target video is acquired. The target video may be any form of video that requires tag classification. For example, the video may be a short video published by a user in a short video platform, or a long video published by a user in another video platform.
In step 102, the target video is input into a plurality of video classification models, respectively, and a plurality of sets of classification prediction results corresponding to the target video are determined.
The video classification model can be any domain classification model, and the number of the video classification models can be any number. For example, a video classification model 1 to a video classification model 10 may be included, the video classification model 1 being used to determine which of the classifications 1 to 10 the target video belongs to based on the probabilities that the target video belongs to the classifications 1 to 10, the video classification model 2 being used to determine which of the classifications 11 to 20 the target video belongs to based on the probabilities that the target video belongs to the classifications 11 to 20, …, the video classification model 10 being used to determine which of the classifications 91 to 100 the target video belongs to based on the probabilities that the target video belongs to the classifications 91 to 100, and so on.
The multiple sets of classification prediction results corresponding to the target video may be formed by prediction classifications output by each video classification model, or may be formed by probability values of respective classifications to which the target video output by each video classification model belongs, or the classification prediction results may include both the probability values and the prediction classifications.
In step 103, a target video feature of the target video is determined according to the multiple sets of classification prediction results.
The method for determining the target video feature according to the multiple sets of classification prediction results may be, for example, intuitively combining and splicing (concat) the multiple sets of classification prediction results into the same array, or selecting a part of the classification prediction results to be spliced into a feature array.
Wherein, when the classification prediction result is a probability that the target video belongs to each classification included in each video classification model, the method for determining the target video feature of the target video according to the multiple sets of classification prediction results may be: and combining the multiple groups of classification prediction results in an N-dimensional array according to a preset sequence, determining the N according to the total number of all the classifications included in the multiple video classification models, and determining the N-dimensional array as the target video characteristic of the target video. For example, if the video classification model includes the video classification models 1 to 4, the classification prediction result output by the video classification model 1 is (0.7,0.2,0.1) and the classification prediction result output by the video classification model 2 is (0.03,0.67,0.3) and the classification prediction result output by the video classification model 3 is (0.7,0.3) and the classification prediction result output by the video classification model 4 is (0.01,0.8,0.09,0.1) and the predetermined sequence is the sequence according to the video classification models 1 to 4, the target video features obtained by merging and splicing are (0.7,0.2,0.1,0.03,0.67,0.3,0.7,0.3,0.01,0.8,0.09,0.1) the 12-dimensional array.
In step 104, the target video features are input into a pre-trained fusion classification model, and classification labels corresponding to the target videos are determined.
In the model training process, the pre-trained fusion classification model processes the training sample video in the same way as in the steps 101 to 103, obtains the video features corresponding to the training sample video through the same processing steps, and inputs the video features into the fusion classification model for training, so that the trained fusion classification model can be obtained.
According to the technical scheme, the classification prediction results corresponding to the target video can be obtained through the plurality of video classification models respectively and used as the classification feature data of the target video, the classification prediction results of the plurality of video classification models are finally fused to be used as the video features of the target video, and then the video classification is identified according to the newly obtained video features, so that the richer classification information of the target video can be obtained, the classification effect of a video classification task is improved, and the classification accuracy of the video is improved.
Fig. 2 is a flowchart illustrating a video classification method according to still another exemplary embodiment of the present disclosure, which further includes steps 201 and 202, as shown in fig. 2.
In step 201, video attribute information of the target video is acquired, wherein the video attribute information includes at least one of video duration, video author, and video author number.
In step 202, a target video feature of the target video is determined according to the plurality of sets of classification prediction results and the video attribute information.
The video attribute information may include other video attribute information in addition to at least one of the above-mentioned video duration, video author, and number of video authors, and the content of the video attribute information is not limited in the present disclosure, as long as it is intrinsic information included in the target video.
The method for determining the target video characteristics of the target video according to the multiple groups of classification prediction results and the video attribute information can also be multiple. If the classification prediction result is the probability that the target video belongs to each classification included in each video classification model, and the method for determining the target video feature of the target video according to the multiple groups of classification prediction results comprises the following steps: combining the multiple sets of classification prediction results in an N-dimensional array according to a preset sequence, determining the N according to the total number of all classifications included in the multiple video classification models, and determining the N-dimensional array as the target video feature of the target video, wherein determining the target video feature of the target video according to the multiple sets of classification prediction results and the video attribute information can be to take each type of information in the video attribute information as one-dimensional data in the array, and combining and splicing the N-dimensional data corresponding to the multiple sets of classification prediction results into new array data.
For example, in the case where the video attribute information includes the video duration, the video author, and the number of video authors, the data corresponding to the video duration may be (60) representing that the video duration is 60 seconds, the data corresponding to the video author may be (12345) representing that the ID of the video author, the data corresponding to the number of video authors may be (100) representing that the number of video authors is 100 tens of thousands, and the like, according to the above example, if the target video feature determined according to the plurality of sets of classification prediction results is (0.7,0.2,0.1,0.03,0.67,0.3,0.7,0.3,0.01,0.8,0.09,0.1), the target video feature may be determined to be (0.7,0.2,0.1,0.03,0.67,0.3,0.7,0.3,0.01,0.8,0.09,0.1, 60, 12345, 100) according to the plurality of sets of classification prediction results and the video attribute information.
In addition, in the process of training the fusion classification model, the processing of the training sample video is the same as that in steps 101, 02, 201 and 202 shown in fig. 2, and the trained fusion classification model can be obtained by inputting the video features corresponding to the training sample video into the fusion classification model for training after the same processing steps are performed.
Through the technical scheme, the classification information of the video can be obtained through a plurality of video classification models, and the inherent video attribute information in the video can be used as the video characteristics of the target video, so that the more abundant classification information of the video can be obtained, the classification effect of a video classification task is further improved, and the classification accuracy of the video is further improved.
In a possible implementation manner, the video classification model includes a first classification model, and video categories corresponding to a plurality of first target classifications included in the first classification model are not identical. The first object classification, i.e. the object classification that the first classification model is able to classify the object video, may for example comprise a conventional primary classification of fun, delight, fashion, travel, parent-child, car, game, music, science and technology etc. or in some special application areas may also comprise a secondary classification such as hand-tour, page-tour, end-tour etc. That is, the first target classification may be all the first-stage classification, or may include a part of the first-stage classification and a part of the second-stage classification. The video category corresponding to the first class classification, namely the video category of the video, for example, the video category corresponding to the game classification is the game category, the video category corresponding to the second class classification is the video category corresponding to the first class classification to which the video category belongs, for example, the video category corresponding to the hand game, the page game, the end game and the like is the game category corresponding to the first class classification game. Under the condition that all the first target classifications are primary classifications, the video classifications corresponding to the first target classifications are different, and under the condition that the first target classifications comprise both primary classifications and secondary classifications, the video classifications corresponding to the first target classifications are not identical.
In a possible implementation manner, the video classification model includes a second classification model, and a plurality of second target classifications included in the second classification model all belong to the same video classification; the video classification model comprises a plurality of second classification models, and the video categories corresponding to the second classification models are different. The second classification model may be, for example, a vertical classification model, the model corresponding to one video classification alone, and all second object classifications included in the model belonging to the video classification to which the model corresponds. For example, the video category may be any of the above-described primary categories such as games, and the second target category may be a secondary category included in each of the game categories such as hand-play, page-play, end-play, and the like. The video classification models may include different video categories corresponding to the second classification model, for example, a second classification model corresponding to a game category, a second classification model corresponding to a food category, a second classification model corresponding to an automobile category, and so on.
In a possible implementation manner, the video classification model includes a third classification model, and a plurality of third object classifications included in the third classification model, where a correlation degree between any two videos belonging to the same third object classification is lower than a first preset threshold. The third classification model may be a weakly correlated model, such as a wind drawing model, in which the content correlation between any two videos classified into the same classification is weak, but even if the content correlation is weak, a part of video feature information in the videos can be represented. The first preset threshold may be set according to the actual situation, as long as the third classification model is a weakly-correlated classification model.
The video classification model can comprise one or more of the first classification model, the second classification model and the third classification model, and can also comprise a plurality of second classification models and/or a plurality of third classification models at the same time, so that more video characteristic information in the target video is extracted through each classification model, the classification effect of a video classification task is further improved, and the classification accuracy of the video is further improved.
Fig. 3 is a flowchart illustrating a training method of a fusion classification model in a video classification method according to still another exemplary embodiment of the present disclosure. As shown in fig. 3, the method comprises steps 301 to 304.
In step 301, a training sample video is acquired.
In step 302, training sample videos are respectively input into a plurality of video classification models to determine a plurality of sets of classification prediction results corresponding to the training sample videos, and classification label labels of the training sample videos are determined according to the classification prediction results of the first classification model.
In step 303, sample video features of the training sample video are determined according to the plurality of sets of classification predictions.
In step 304, the sample video features are input into the fused classification model to train the fused classification model.
Under the condition that the video classification model comprises the first classification model, the classification result output by the first classification model can be directly used as the classification label of the training sample video to label the training sample video, so that a large amount of unlabeled videos can be used as the training sample video to train the fusion classification model.
The fusion classification model may be GBDT (Gradient Boosting Decision Tree) or DNN (Deep Neural Network), or any other supervised machine learning model, and the model type of the fusion classification model is not limited in this disclosure, and the model content of the multiple video classification models is not limited.
Wherein the training method may further include obtaining video attribute information of the training sample video, and the determining the sample video feature of the training sample video in step 303 may further include: and determining sample video characteristics of the training sample video according to the multiple groups of classification prediction results and the video attribute information of the training sample video.
Fig. 4 is a block diagram illustrating a video classification apparatus according to an exemplary embodiment of the present disclosure. As shown in fig. 4, the apparatus includes: a first acquisition module 10, configured to acquire a target video; a first determining module 20, configured to input the target video into a plurality of video classification models, and determine a plurality of sets of classification prediction results corresponding to the target video; a second determining module 30, configured to determine a target video feature of the target video according to the multiple sets of classification prediction results; and a third determining module 40, configured to input the target video feature into a pre-trained fusion classification model, and determine a classification label corresponding to the target video.
According to the technical scheme, the classification prediction results corresponding to the target video can be obtained through the plurality of video classification models respectively and used as the classification feature data of the target video, the classification prediction results of the plurality of video classification models are finally fused to be used as the video features of the target video, and then the video classification is identified according to the newly obtained video features, so that the richer classification information of the target video can be obtained, the classification effect of a video classification task is improved, and the classification accuracy of the video is improved.
Fig. 5 is a block diagram illustrating a video classification apparatus according to still another exemplary embodiment of the present disclosure, and the apparatus further includes, as shown in fig. 5: a second obtaining module 50, configured to obtain video attribute information of the target video, where the video attribute information includes at least one of a video duration, a video author, and a number of video authors; the second determining module 30 is further configured to: and determining target video characteristics of the target video according to the multiple groups of classification prediction results and the video attribute information.
In a possible implementation manner, the video classification model includes a first classification model, and video categories corresponding to a plurality of first target classifications included in the first classification model are not identical.
In a possible implementation manner, the video classification model includes a second classification model, and a plurality of second target classifications included in the second classification model all belong to the same video classification; the video classification model comprises a plurality of second classification models, and the video categories corresponding to the second classification models are different.
In a possible implementation manner, the video classification model includes a third classification model, and a plurality of third object classifications included in the third classification model, where a correlation degree between any two videos belonging to the same third object classification is lower than a first preset threshold.
In a possible implementation manner, the classification prediction result is a probability that the target video belongs to each classification included in each video classification model; the second determining module 30 is further configured to: merging the multiple groups of classification prediction results into an N-dimensional group according to a preset sequence, and determining N according to the total number of all the classifications included in the multiple video classification models; the N-dimensional array is determined as the target video feature of the target video.
In one possible implementation, the fused classification model is trained by: acquiring a training sample video; respectively inputting training sample videos into a plurality of video classification models to determine a plurality of groups of classification prediction results corresponding to the training sample videos, and determining classification label labels of the training sample videos according to the classification prediction results of the first classification model; determining sample video features of the training sample video according to the multiple groups of classification prediction results; and inputting the sample video features into the fusion classification model to train the fusion classification model.
Referring now to fig. 6, a schematic diagram of an electronic device 600 suitable for use in implementing embodiments of the present disclosure is shown. The terminal 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., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 6 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 6, the electronic device 600 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 601, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
In general, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 shows an electronic device 600 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present disclosure, the processes described above with reference to 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 non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via communication means 609, or from storage means 608, or from ROM 602. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 601.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 context of this 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 the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some implementations, the clients, servers may communicate 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 networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated 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 target video; respectively inputting the target video into a plurality of video classification models, and determining a plurality of groups of classification prediction results corresponding to the target video; determining target video characteristics of the target video according to the multiple groups of classification prediction results; and inputting the target video features into a pre-trained fusion classification model, and determining classification labels corresponding to the target videos.
Computer program code for carrying out operations of the present disclosure may be written in 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts 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 in software or hardware. The name of the module is not limited to the module itself in some cases, and for example, the first acquisition module may also be described as "a module that acquires a target video".
The functions described above herein 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: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), 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. The 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.
In accordance with one or more embodiments of the present disclosure, example 1 provides a video classification method, the method comprising: acquiring a target video; respectively inputting the target video into a plurality of video classification models, and determining a plurality of groups of classification prediction results corresponding to the target video; determining target video characteristics of the target video according to the multiple groups of classification prediction results; and inputting the target video features into a pre-trained fusion classification model, and determining classification labels corresponding to the target videos.
In accordance with one or more embodiments of the present disclosure, example 2 provides the method of example 1, the method further comprising: acquiring video attribute information of the target video, wherein the video attribute information comprises at least one of video duration, video authors and video author fan numbers; the determining the target video characteristics of the target video according to the multiple groups of classification prediction results comprises: and determining target video characteristics of the target video according to the multiple groups of classification prediction results and the video attribute information.
In accordance with one or more embodiments of the present disclosure, example 3 provides the method of example 1, wherein the video classification model includes a first classification model, and video categories corresponding to a plurality of first target classifications included in the first classification model are not identical.
In accordance with one or more embodiments of the present disclosure, example 4 provides the method of example 1, wherein the video classification model includes a second classification model, and wherein a plurality of second target classifications included in the second classification model all belong to a same video classification; the video classification model comprises a plurality of second classification models, and the video categories corresponding to the second classification models are different.
According to one or more embodiments of the present disclosure, example 5 provides the method of example 1, wherein the video classification model includes a third classification model, and a plurality of third object classifications included in the third classification model, wherein a correlation degree between any two videos belonging to the same third object classification is lower than a first preset threshold.
In accordance with one or more embodiments of the present disclosure, example 6 provides the method of example 1, the classification prediction result being a probability that the target video belongs to each classification included in each of the video classification models, respectively; the determining the target video characteristics of the target video according to the multiple groups of classification prediction results comprises: merging the multiple groups of classification prediction results into an N-dimensional group according to a preset sequence, and determining N according to the total number of all the classifications included in the multiple video classification models; the N-dimensional array is determined as the target video feature of the target video.
In accordance with one or more embodiments of the present disclosure, example 7 provides the method of example 1, the fused classification model being trained by: acquiring a training sample video; respectively inputting training sample videos into a plurality of video classification models to determine a plurality of groups of classification prediction results corresponding to the training sample videos, and determining classification label labels of the training sample videos according to the classification prediction results of the first classification model; determining sample video features of the training sample video according to the multiple groups of classification prediction results; and inputting the sample video features into the fusion classification model to train the fusion classification model.
In accordance with one or more embodiments of the present disclosure, example 8 provides a video classification apparatus, the apparatus comprising: the first acquisition module is used for acquiring a target video; the first determining module is used for respectively inputting the target video into a plurality of video classification models and determining a plurality of groups of classification prediction results corresponding to the target video; the second determining module is used for determining target video characteristics of the target video according to the multiple groups of classification prediction results; and the third determining module is used for inputting the target video characteristics into a pre-trained fusion classification model and determining classification labels corresponding to the target videos.
According to one or more embodiments of the present disclosure, example 9 provides a computer-readable medium having stored thereon a computer program which, when executed by a processing device, implements the steps of the method of any of examples 1-7.
In accordance with one or more embodiments of the present disclosure, example 10 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 implement the steps of the method of any one of examples 1-7.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).
Moreover, although 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. In 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 limiting the scope of the present 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 example forms of implementing the claims. The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Claims (8)
1. A method of video classification, the method comprising:
acquiring a target video;
respectively inputting the target video into a plurality of video classification models, and determining a plurality of groups of classification prediction results corresponding to the target video;
determining target video characteristics of the target video according to the multiple groups of classification prediction results;
inputting the target video features into a pre-trained fusion classification model, and determining classification labels corresponding to the target videos;
the method further comprises the steps of:
acquiring video attribute information of the target video, wherein the video attribute information comprises at least one of video duration, video authors and video author fan numbers;
the determining the target video characteristics of the target video according to the multiple groups of classification prediction results comprises:
determining target video characteristics of the target video according to the multiple groups of classification prediction results and the video attribute information;
the video classification model comprises a second classification model, and a plurality of second target classifications contained in the second classification model all belong to the same video classification;
the video classification model comprises a plurality of second classification models, and the video categories corresponding to the second classification models are different.
2. The method of claim 1, wherein the video classification model includes a first classification model, and wherein video categories corresponding to a plurality of first target classifications included in the first classification model are not identical.
3. The method according to claim 1, wherein the video classification model includes a third classification model, and the third classification model includes a plurality of third object classifications, and wherein a correlation between any two videos belonging to the same third object classification is lower than a first preset threshold.
4. The method of claim 1, wherein the classification prediction result is a probability that the target video belongs to each classification included in each of the video classification models, respectively;
the determining the target video characteristics of the target video according to the multiple groups of classification prediction results comprises:
merging the multiple groups of classification prediction results into an N-dimensional group according to a preset sequence, and determining N according to the total number of all the classifications included in the multiple video classification models;
the N-dimensional array is determined as the target video feature of the target video.
5. The method of claim 2, wherein the fused classification model is trained by:
acquiring a training sample video;
respectively inputting training sample videos into a plurality of video classification models to determine a plurality of groups of classification prediction results corresponding to the training sample videos, and determining classification label labels of the training sample videos according to the classification prediction results of the first classification model;
determining sample video features of the training sample video according to the multiple groups of classification prediction results;
and inputting the sample video features into the fusion classification model to train the fusion classification model.
6. A video classification device, the device comprising:
the first acquisition module is used for acquiring a target video;
the first determining module is used for respectively inputting the target video into a plurality of video classification models and determining a plurality of groups of classification prediction results corresponding to the target video;
the second determining module is used for determining target video characteristics of the target video according to the multiple groups of classification prediction results;
the third determining module is used for inputting the target video characteristics into a pre-trained fusion classification model and determining classification labels corresponding to the target videos;
the second acquisition module is used for acquiring video attribute information of the target video, wherein the video attribute information comprises at least one of video duration, video authors and video author fan numbers;
the second determining module is further configured to: determining target video characteristics of the target video according to the multiple groups of classification prediction results and the video attribute information;
the video classification model comprises a second classification model, and a plurality of second target classifications contained in the second classification model all belong to the same video classification;
the video classification model comprises a plurality of second classification models, and the video categories corresponding to the second classification models are different.
7. A computer readable medium on which a computer program is stored, characterized in that the program, when being executed by a processing device, carries out the steps of the method according to any one of claims 1-5.
8. An electronic device, comprising:
a storage device having a computer program stored thereon;
processing means for executing said computer program in said storage means to carry out the steps of the method according to any one of claims 1-5.
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