CN111797912B - System and method for identifying film age type and construction method of identification model - Google Patents
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Abstract
The invention provides a system and a method for identifying film age types and a method for constructing an identification model. A system for film year type identification, comprising: a trained film age type identification model; the computing device, the storage device and the input-output device constitute a computer device for automatic identification processes; a convolutional neural network system formed by decoding a digital film, capturing a picture and preprocessing the picture; and a classifier capable of analyzing the film age type according to the model reasoning result. With the system and method, a computing device may automatically identify the chronological type of a movie after obtaining a digital movie from a storage device. Compared with manual identification, the identification by using the computing equipment has the advantages of reliability, rapidness, cheapness, batch processing and the like.
Description
Technical Field
The invention relates to a system and a method for identifying film age types and a method for constructing an identification model, and belongs to the technical field of information technology.
Background
In recent years, video services have rapidly developed, and activities such as chasing dramas and watching digital televisions become an important entertainment mode for people. Video providers often store a large number of movies, and how to automatically, quickly, reliably, and batchwise label these movies with ages, titles, genres, etc. is of great importance in helping the providers provide good quality video services.
Disclosure of Invention
The invention aims to provide a system and a method for identifying film age types and a method for constructing an identification model.
The invention aims to achieve the aim, and the aim is achieved by the following technical scheme:
a system for film year type identification, comprising:
a trained film age type identification model;
the computing device, the storage device and the input-output device constitute a computer device for automatic identification processes;
a convolutional neural network system formed by decoding a digital film, capturing a picture and preprocessing the picture;
and a classifier capable of analyzing the film age type according to the model reasoning result.
Based on the film age type identification system, the convolutional neural network system is of a VGG-16 network structure.
A film age type identification method by the system comprises the following steps:
the computing equipment reads a film to be identified from the storage equipment, and cuts off the first a minute and the last b minute of the film, and then intercepts M pictures at uniform time intervals;
after preprocessing M pictures, respectively inputting the M pictures into a convolutional neural network;
for each picture input, the convolutional neural network can infer an N-dimensional probability vector, and each dimension corresponds to the probability that the picture belongs to the corresponding age type;
after all reasoning of the M pictures is completed, the system for identifying the film age type inputs M probability vectors into the classifier.
The method for constructing the film age type identification model is characterized by comprising the following steps of:
s1, constructing a training set and a verification set: preparing a large number of digital films belonging to N types of years, and uniformly intercepting pictures from each film time interval as training data and verification data of the type;
s2, building a convolutional neural network: using a deep convolutional network for image classification tasks as the main body of the network;
s3, training of a convolutional neural network: freezing parameters of a convolution base, training a classifier, wherein a first layer of the classifier uses Dropout; after the classifier reaches a better accuracy through multiple rounds of training, the bottom layer of the convolution base is thawed, fine tuning training is carried out, and the accuracy of the verification network on the verification set is verified.
According to the method for constructing the film age type identification model, all training pictures and verification pictures are preprocessed, and the method comprises the following steps: 1) Scaling to an input size required by the neural network; 2) Subtracting the average RGB value of the whole picture dataset from the pixels of each picture; 3) The pixel values of RGB are divided by 255 such that the RGB values are between 0 and 1.
6. A method of constructing a film year type identification model as claimed in claim 3, wherein: the classifier analyzes the chronology type of the film according to the following algorithm: any playing time t of the video stream corresponds to a frameTaking the frame as the input of the model, an N-dimensional probability vector can be obtained>Each dimension of the vector corresponds to a chronology type respectively;
introduction evaluationDividing a function into ；
Integrating the scoring functions of all playing moments to obtain total scoresThe total score is an N-dimensional vector whose dimensions are defined as +.>And each dimension corresponds to one year type;
and taking the age type with the largest total score as the classification result of the video.
The invention has the advantages that: by the system and the method, the computing device can automatically identify the age type of the film after acquiring the digital film from the storage device, and compared with manual identification, the method has the advantages of reliability, rapidness, low cost, batch processing and the like.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
Fig. 1 is a schematic flow chart of a method for constructing a film age type recognition model according to the present invention.
Fig. 2 is a flowchart of a film age type identification method according to the present invention.
Fig. 3 is a schematic diagram of a connection of the film age type identification system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
A system for chronology type recognition, comprising: a trained film age type identification model; the computing device, the storage device and the input-output device constitute a computer device for automatic identification processes; a convolutional neural network system formed by decoding a digital film, capturing a picture and preprocessing the picture; and a classifier capable of analyzing the film age type according to the model reasoning result.
A method for identifying the film age type by the system, which marks the film to be identified with an age type label, wherein the actual age type of the film to be identified must be one of N preset age types, which can be ancient dress, republic, contemporary and the like, includes the following steps:
the computing equipment reads a film to be identified from the storage equipment, and cuts off the first a minute and the last b minute of the film, and then intercepts M pictures at uniform time intervals;
after preprocessing M pictures, respectively inputting the M pictures into a convolutional neural network;
picture preprocessing includes, but is not limited to, subtracting the average RGB value of all pixels from the RGB value of each pixel of the picture, multiplying the RGB values of all pixels by 1/255 so that the values fall within the interval of 0-1, scaling the picture to a certain fixed size, and so on;
for each picture input, the convolutional neural network can infer an N-dimensional probability vector, and each dimension corresponds to the probability that the picture belongs to the corresponding age type;
after all reasoning of the M pictures is completed, the system for identifying the film age type inputs M probability vectors into the classifier.
The method for constructing the film age type identification model comprises the following steps:
s1, constructing a training set and a verification set: for each type of year, S digital films are prepared, and S is large enough; for each film, firstly pinching off the first a minutes and the last b minutes to ensure that the film head and the film tail are pinched off; intercepting T pictures at equal time intervals as the data of the type; thus, each type can obtain S multiplied by T pictures, and the S multiplied by T pictures are distributed to a training set and a verification set according to a certain proportion;
s2, building a convolutional neural network: using a deep convolutional network for image classification tasks as the main body of the network, for example, a VGG16 network, initializing the network by using parameters of the VGG16 network trained on a large picture data set, for example, downloading the trained VGG16 network by using Keras;
s3, training of a convolutional neural network: freezing parameters of a convolution base, training a classifier, wherein a first layer of the classifier uses Dropout for reducing the problem of overfitting; after the classifier reaches a better accuracy through multiple rounds of training, the bottom layer of the convolution base is thawed, fine tuning training is carried out, and the accuracy of the verification network on the verification set is verified.
All training pictures and verification pictures should be preprocessed, including the following steps: 1) Scaling to an input size required by the neural network; 2) Subtracting the average RGB value of the whole picture dataset from the pixels of each picture; 3) The pixel values of RGB are divided by 255 such that the RGB values are between 0 and 1.
The classifier analyzes the chronology type of the film according to the following algorithm: introducing an N-dimensional probability vectorFunction of->Wherein->Hereafter will be->Called scoring function;
for a video stream S with a duration T, each playing time T corresponds to a frameF is input into the network to obtain a probability vector +.>The scoring function is a unitary function of the probability vector, and as known from the chain rule, the scoring function is also a unitary function r=r (t) at the playing time t;
the scores of all moments of the video stream S are integrated to obtain an overall scoreThe function is an N-dimensional vector, and
thus, the first and second light sources are connected,i.e. film type, wherein->The operation finds the subscript of the vector.
For digital video, the playing time is discrete, in other words, a digital film with a frame rate of 25fps contains 25 frames per second, instead of a myriad of frames. In this case, the integral expression degenerates into a sum expression, i.e
T on the left of the equal sign is a constant and has no effect on the classification result, and for convenience the expression of the total scoring function can be redefined as
The movie is tagged with the corresponding year type if the value of that dimension is large.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. A film year type identification method comprising a film year type identification system, said system comprising:
the computing device, the storage device and the input-output device constitute a computer device for automatic identification processes;
a convolutional neural network system formed by a digital film decoding, screenshot, picture preprocessing and trained film age type recognition model; the recognition model is a VGG-16 network structure;
the classifier can analyze the film age type according to the model reasoning result;
the method comprises the following steps:
the computing equipment reads a film to be identified from the storage equipment, and cuts off the first a minute and the last b minute of the film, and then intercepts M pictures at uniform time intervals;
after preprocessing M pictures, respectively inputting the M pictures into a convolutional neural network;
for each picture input, the convolutional neural network can infer an N-dimensional probability vector, and each dimension corresponds to the probability that the picture belongs to the corresponding age type;
after all reasoning of the M pictures is completed, the system for identifying the film age type inputs M probability vectors into a classifier;
the classifier analyzes the chronology type of the film according to the following algorithm: any playing time t of the video stream corresponds to a frameTaking the frame as the input of the model, an N-dimensional probability vector can be obtained>Each dimension of the vector corresponds to a chronology type respectively, and a scoring function is introduced>Wherein->Integrating the scoring functions of all playing moments to obtain total score +.>The total score is an N-dimensional vector whose dimensions are defined as +.>
And each dimension corresponds to one age type, and the age type with the largest total score is taken as the classification result of the video.
2. The film year type identification method as set forth in claim 1, comprising the steps of:
s1, constructing a training set and a verification set: preparing a large number of digital films belonging to N types of years, and uniformly intercepting pictures from each film time interval as training data and verification data of the type;
s2, building a convolutional neural network: using a deep convolutional network for image classification tasks as the main body of the network;
s3, training a convolutional neural network: freezing parameters of a convolution base, training a classifier, wherein a first layer of the classifier uses Dropout; after the classifier reaches a better accuracy through multiple rounds of training, the bottom layer of the convolution base is thawed, fine tuning training is carried out, and the accuracy of the verification network on the verification set is verified.
3. The film year type identification method according to claim 2, wherein: the preprocessing of the M pictures comprises the following steps: 1) Scaling to an input size required by the neural network; 2) Subtracting the average RGB value of the whole picture dataset from the pixels of each picture; 3) The pixel values of RGB are divided by 255 such that the RGB values are between 0 and 1.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103229223A (en) * | 2010-09-28 | 2013-07-31 | 国际商业机器公司 | Providing answers to questions using multiple models to score candidate answers |
| CN105844251A (en) * | 2016-03-31 | 2016-08-10 | 乐视控股（北京）有限公司 | Cartoon video identification method and device |
| CN107067020A (en) * | 2016-12-30 | 2017-08-18 | 腾讯科技（上海）有限公司 | Image identification method and device |
| CN107194419A (en) * | 2017-05-10 | 2017-09-22 | 百度在线网络技术（北京）有限公司 | Video classification methods and device, computer equipment and computer-readable recording medium |
| CN108154134A (en) * | 2018-01-11 | 2018-06-12 | 天格科技（杭州）有限公司 | Internet live streaming pornographic image detection method based on depth convolutional neural networks |
| CN109753984A (en) * | 2017-11-07 | 2019-05-14 | 北京京东尚科信息技术有限公司 | Video classification methods, device and computer readable storage medium |
| CN109919031A (en) * | 2019-01-31 | 2019-06-21 | 厦门大学 | A kind of Human bodys' response method based on deep neural network |
| CN110147700A (en) * | 2018-05-18 | 2019-08-20 | 腾讯科技（深圳）有限公司 | Video classification methods, device, storage medium and equipment |
| CN110766096A (en) * | 2019-10-31 | 2020-02-07 | 北京金山云网络技术有限公司 | Video classification method and device and electronic equipment |
| CN110866593A (en) * | 2019-11-05 | 2020-03-06 | 西南交通大学 | Highway severe weather identification method based on artificial intelligence |
| CN110991246A (en) * | 2019-10-31 | 2020-04-10 | 天津市国瑞数码安全系统股份有限公司 | Video detection method and system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170053024A1 (en) * | 2014-04-28 | 2017-02-23 | Hewlett Packard Enterprise Development Lp | Term chain clustering |
| US10402697B2 (en) * | 2016-08-01 | 2019-09-03 | Nvidia Corporation | Fusing multilayer and multimodal deep neural networks for video classification |
-
2020
- 2020-06-23 CN CN202010580262.5A patent/CN111797912B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103229223A (en) * | 2010-09-28 | 2013-07-31 | 国际商业机器公司 | Providing answers to questions using multiple models to score candidate answers |
| CN105844251A (en) * | 2016-03-31 | 2016-08-10 | 乐视控股（北京）有限公司 | Cartoon video identification method and device |
| CN107067020A (en) * | 2016-12-30 | 2017-08-18 | 腾讯科技（上海）有限公司 | Image identification method and device |
| CN107194419A (en) * | 2017-05-10 | 2017-09-22 | 百度在线网络技术（北京）有限公司 | Video classification methods and device, computer equipment and computer-readable recording medium |
| CN109753984A (en) * | 2017-11-07 | 2019-05-14 | 北京京东尚科信息技术有限公司 | Video classification methods, device and computer readable storage medium |
| CN108154134A (en) * | 2018-01-11 | 2018-06-12 | 天格科技（杭州）有限公司 | Internet live streaming pornographic image detection method based on depth convolutional neural networks |
| CN110147700A (en) * | 2018-05-18 | 2019-08-20 | 腾讯科技（深圳）有限公司 | Video classification methods, device, storage medium and equipment |
| CN109919031A (en) * | 2019-01-31 | 2019-06-21 | 厦门大学 | A kind of Human bodys' response method based on deep neural network |
| CN110766096A (en) * | 2019-10-31 | 2020-02-07 | 北京金山云网络技术有限公司 | Video classification method and device and electronic equipment |
| CN110991246A (en) * | 2019-10-31 | 2020-04-10 | 天津市国瑞数码安全系统股份有限公司 | Video detection method and system |
| CN110866593A (en) * | 2019-11-05 | 2020-03-06 | 西南交通大学 | Highway severe weather identification method based on artificial intelligence |
Non-Patent Citations (4)
| Title |
|---|
| Video classification and recommendation based on affective analysis of viewers;Sicheng Zhao等;《Neurocomputing》;第119卷;第101–110页 * |
| 基于多分类器集成的模式识别研究;潘翔;《中国优秀硕士学位论文全文数据库 信息科技辑》(第（2002）02期);I138-526 * |
| 基于智能机顶盒视频点播系统的设计与实现;朱宁;《中国优秀硕士学位论文全文数据库 信息科技辑》(第（2015）07期);I136-439 * |
| 基于视频流行度及视频分类标签的Top-N推荐;周瑞环;《中国优秀硕士学位论文全文数据库 信息科技辑》(第（2018）10期);I138-1031 * |
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