CN111797912A - System and method for identifying film generation type and construction method of identification model - Google Patents

System and method for identifying film generation type and construction method of identification model Download PDF

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CN111797912A
CN111797912A CN202010580262.5A CN202010580262A CN111797912A CN 111797912 A CN111797912 A CN 111797912A CN 202010580262 A CN202010580262 A CN 202010580262A CN 111797912 A CN111797912 A CN 111797912A
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杨唤晨
徐杰
谢恩鹏
刘永辉
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Abstract

The invention provides a system and a method for identifying the type of a film generation and a construction method of an identification model. A system for chronological type identification of a movie, comprising: a trained film generation type identification model; the computing device, the storage device and the input-output device constitute a computer device for automatically identifying the process; a convolution neural network system formed by digital film decoding, screenshot and picture preprocessing; and the classifier can analyze the film generation type according to the model reasoning result. By the system and the method, the generation type of the film can be automatically identified after the computing device obtains the digital film from the storage device. Compared with manual identification, identification by using a computing device has the advantages of reliability, rapidness, low cost, batch processing and the like.

Description

System and method for identifying film generation type and construction method of identification model
Technical Field
The invention relates to a system and a method for identifying the year type of a film and a construction method of an identification model, belonging to the technical field of information.
Background
In recent years, video services have been rapidly developed, and activities such as tracing a network play and watching digital television have become important entertainment modes for people. Video service providers often store a large number of films, how to automatically, quickly, reliably and batch label the films with the labels of the year, subject, genre and the like is of great significance for helping the service providers to provide high-quality video services.
Disclosure of Invention
The invention aims to provide a system and a method for identifying the type of a film generation and a construction method of an identification model.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a system for chronological type identification of a movie, comprising:
a trained film generation type identification model;
the computing device, the storage device and the input-output device constitute a computer device for automatically identifying the process;
a convolution neural network system formed by digital film decoding, screenshot and picture preprocessing;
and the classifier can analyze the film generation type according to the model reasoning result.
On the basis of the system for identifying the film generation types, the convolutional neural network system is of a VGG-16 network structure.
A method for identifying the generation type of a film by the system comprises the following steps:
reading a movie to be identified from a storage device by the computing device, and cutting off M pictures at uniform time intervals after cutting off the first a minutes and the last b minutes of the movie;
after preprocessing the M pictures, respectively inputting the M pictures into a convolutional neural network;
for each picture input, the convolutional neural network can deduce an N-dimensional probability vector, and each dimension corresponds to the probability that the picture belongs to the corresponding age type;
after all M pictures are reasoned, the system for identifying the film generation type inputs M probability vectors into the classifier.
A method for constructing the film generation type identification model is characterized by comprising the following steps:
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 construction of the convolutional neural network: using a deep convolutional network for image classification tasks as the subject of the network;
s3 training of the convolutional neural network: freezing parameters of a convolution base, training a classifier, and using Dropout at the first layer of the classifier; after multiple rounds of training, the classifier achieves better accuracy, the bottom layer of the convolution base is unfrozen, fine tuning training is carried out, and the accuracy of the verification network on the verification set is verified.
According to the construction method of the film generation type identification model, all training pictures and verification pictures are required to be preprocessed, and the method comprises the following steps: 1) scaling to the input size required by the neural network; 2) subtracting the average RGB value of the whole picture data set from the pixel of each picture; 3) the pixel values of RGB are divided by 255 so that the RGB values are between 0 and 1.
6. The method for constructing a model for identifying a chronological genre of a movie as claimed in claim 3, wherein: the classifier analyzes the chronological type of the film according to the following algorithm: any playing time t of the video stream corresponds to one frame
Figure DEST_PATH_IMAGE001
Taking the frame as the input of the model can obtain an N-dimensional probability vector
Figure 970120DEST_PATH_IMAGE002
Each dimension of the vector corresponds to a year type respectively;
introducing a scoring function wherein
Figure DEST_PATH_IMAGE003
；
Integrating the scoring functions of all playing moments to obtain total score
Figure 949577DEST_PATH_IMAGE004
The total score is an N-dimensional vector whose dimensions are defined by
Figure DEST_PATH_IMAGE005
And each dimension corresponds to a year type;
and taking the age type with the maximum total score as the classification result of the video.
The invention has the advantages that: by the system and the method, the generation type of the film can be automatically identified after the computing device obtains the digital film from the storage device, and compared with manual identification, the identification by using the computing device has the advantages of reliability, rapidness, low cost, batch processing and the like.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a schematic flow chart of a method for constructing a film generation type identification model according to the present invention.
Fig. 2 is a flowchart illustrating a film generation type identification method according to the present invention.
Fig. 3 is a schematic diagram of the connection of the film generation type identification system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A system of chronological type identification, comprising: a trained film generation type identification model; the computing device, the storage device and the input-output device constitute a computer device for automatically identifying the process; a convolution neural network system formed by digital film decoding, screenshot and picture preprocessing; and the classifier can analyze the film generation type according to the model reasoning result.
A kind of said system carries on the identifying method of the film year type, this method will wait to discern the film and print year type label, wait to discern the actual year type of the film must be one in N year types preserved, these year types can be types such as ancient clothes, republic of China, contemporary, the method includes the following steps:
reading a movie to be identified from a storage device by the computing device, and cutting off M pictures at uniform time intervals after cutting off the first a minutes and the last b minutes of the movie;
after preprocessing the M pictures, respectively inputting the M pictures into a convolutional neural network;
picture pre-processing 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 1/255 the RGB values of all pixels so that the values fall within the interval of 0-1, scaling the picture to some fixed size, etc.;
for each picture input, the convolutional neural network can deduce an N-dimensional probability vector, and each dimension corresponds to the probability that the picture belongs to the corresponding age type;
after all M pictures are reasoned, the system for identifying the film generation type inputs M probability vectors into the classifier.
A method for constructing the film generation type identification model comprises the following steps:
s1, constructing a training set and a verification set: for each type of era, S digital movies are prepared, S being large enough; for each movie, the first a minutes and the last b minutes are firstly pinched off to ensure that the leader and the trailer are pinched off; intercepting T pictures at equal time intervals to serve as data of the type; thus, S multiplied by T pictures can be obtained from each type and are distributed to a training set and a verification set according to a certain proportion;
s2 construction of the convolutional neural network: using a deep convolutional network for image classification task as the main body of the network, for example, a VGG16 network, using parameters of a VGG16 network trained on a large picture data set, initializing its own network, for example, using a Keras download trained VGG16 network;
s3 training of the convolutional neural network: freezing parameters of a convolution base, training a classifier, wherein Dropout is used in the first layer of the classifier to reduce the overfitting problem; after multiple rounds of training, the classifier achieves better accuracy, the bottom layer of the convolution base is unfrozen, 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 are required to be preprocessed, and the method comprises the following steps: 1) scaling to the input size required by the neural network; 2) subtracting the average RGB value of the whole picture data set from the pixel of each picture; 3) the pixel values of RGB are divided by 255 so that the RGB values are between 0 and 1.
The classifier analyzes the chronological type of the film according to the following algorithm: introducing N-dimensional probability vectors
Figure 994893DEST_PATH_IMAGE006
Function of (2)
Figure DEST_PATH_IMAGE007
Wherein
Figure 709210DEST_PATH_IMAGE008
Hereinafter, will be described
Figure DEST_PATH_IMAGE009
Referred to as a scoring function;
for a video stream S with a duration T, each playing time T corresponds to a frame
Figure 585899DEST_PATH_IMAGE010
Inputting F into the network to obtain a probability vector
Figure DEST_PATH_IMAGE011
The scoring function is a unitary function of the probability vector, and the scoring function is also a unitary function during playing according to the chain ruleA univariate function R = R (t) at moment t;
integrating the scores of all the moments of the video stream S to obtain a total score
Figure 470678DEST_PATH_IMAGE012
The function is an N-dimensional vector, an
As a result of this, the number of the,
Figure DEST_PATH_IMAGE013
i.e. film type, wherein
Figure 127925DEST_PATH_IMAGE014
And calculating 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, rather than an infinite number of frames. In this case, the integral expression is degenerated into a summation expression, i.e.
Figure 389142DEST_PATH_IMAGE016
T on the left of the equal sign is a constant, which has no influence on the classification result, and for convenience, the expression of the total scoring function can be redefined as
Figure DEST_PATH_IMAGE017
Figure 385916DEST_PATH_IMAGE018
If the value of that dimension is large, the film is labeled with the corresponding genre.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (6)

1. A system for identifying a chronological type of a movie, comprising:
a trained film generation type identification model;
the computing device, the storage device and the input-output device constitute a computer device for automatically identifying the process;
a convolution neural network system formed by digital film decoding, screenshot and picture preprocessing;
and the classifier can analyze the film generation type according to the model reasoning result.
2. The system for dating type of film as claimed in claim 1, wherein: the convolutional neural network system is of a VGG-16 network structure.
3. A method for identifying the chronological type of a film according to the system of claim 1 or 2, comprising the steps of:
reading a movie to be identified from a storage device by the computing device, and cutting off M pictures at uniform time intervals after cutting off the first a minutes and the last b minutes of the movie;
after preprocessing the M pictures, respectively inputting the M pictures into a convolutional neural network;
for each picture input, the convolutional neural network can deduce an N-dimensional probability vector, and each dimension corresponds to the probability that the picture belongs to the corresponding age type;
after all M pictures are reasoned, the system for identifying the film generation type inputs M probability vectors into the classifier.
4. A method for constructing a model for identifying an era type of a movie as claimed in claim 1 or 2, 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 construction of the convolutional neural network: using a deep convolutional network for image classification tasks as the subject of the network;
s3 training of the convolutional neural network: freezing parameters of a convolution base, training a classifier, and using Dropout at the first layer of the classifier; after multiple rounds of training, the classifier achieves better accuracy, the bottom layer of the convolution base is unfrozen, fine tuning training is carried out, and the accuracy of the verification network on the verification set is verified.
5. The method for constructing a model for identifying a chronological genre of a movie as claimed in claim 3, wherein: all training pictures and verification pictures are required to be preprocessed, and the method comprises the following steps: 1) scaling to the input size required by the neural network; 2) subtracting the average RGB value of the whole picture data set from the pixel of each picture; 3) the pixel values of RGB are divided by 255 so that the RGB values are between 0 and 1.
6. The method for constructing a model for identifying a chronological genre of a movie as claimed in claim 3, wherein: the classifier analyzes the chronological type of the film according to the following algorithm:
any playing time t of the video stream corresponds to one frame
Figure DEST_PATH_IMAGE002
Taking the frame as the input of the model can obtain an N-dimensional probability vector
Figure DEST_PATH_IMAGE004
Each dimension of the vector corresponds to a year type respectively;
introducing a scoring function
Figure DEST_PATH_IMAGE006
Wherein
Figure DEST_PATH_IMAGE008
；
Integrating the scoring functions of all playing moments to obtain total score
Figure DEST_PATH_IMAGE010
The total score is an N-dimensional vector whose dimensions are defined by
Figure DEST_PATH_IMAGE012
And each dimension corresponds to a year type;
and taking the age type with the maximum total score as the classification result of the video.
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