Video classification method and apparatus

Patent metadata

Field	Value
Publication number	US-2017228618-A1
Application number	US-201715495541-A
Country	US
Kind code	A1
Filing date	Apr 24, 2017
Priority date	Oct 24, 2014
Publication date	Aug 10, 2017
Grant date	—

Abstract

A video classification method and apparatus are provided in embodiments of the present invention. The method includes: establishing a neural network classification model according to a relationship between features of video samples and a semantic relationship of the video samples; obtaining a feature combination of a to-be-classified video file; and classifying the to-be-classified video file by using the neural network classification model and the feature combination of the to-be-classified video file The neural network classification model is established according to the relationship between the features of the video samples and the semantic relationship of the video samples, and the relationship between the features and the semantic relationship are fully considered. Therefore, video classification accuracy are improved.

First claim

1 . A video classification method, comprising:
establishing a neural network classification model according to (a) a relationship between features of video samples and (b) a semantic relationship of the video samples; obtaining a feature combination of a to-be-classified video file; and classifying the to-be-classified video file by using the neural network classification model and the feature combination of the to-be-classified video file.

2 . The method according to claim 1 , wherein the establishing the neural network classification model according to the relationship between the features of the video samples and the semantic relationship of the video samples comprises:
obtaining a weight matrix of a neural network classification model fusion layer and a weight matrix of a neural network classification model classification layer according to the relationship between the features of the video samples and the semantic relationship of the video samples; and establishing the neural network classification model according to the weight matrix of the neural network classification model fusion layer and the weight matrix of the neural network classification model classification layer.

3 . The method according to claim 2 , wherein the obtaining the weight matrix of the neural network classification model fusion layer and the weight matrix of the neural network classification model classification layer according to the relationship between the features of the video samples and the semantic relationship of the video samples comprises:
obtaining the weight matrix of the neural network classification model fusion layer and the weight matrix of the neural network classification model classification layer by optimizing a target function, wherein the target function is:

min

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wherein ζ represents a deviation between a predictor and a real value of the video samples, ζ 1 represents a preset first weight coefficient, ζ 2 represents a preset second weight coefficient, W E represents the weight matrix of the neural network classification model fusion layer, each column of W E corresponds to a type of feature, W L-1 represents the weight matrix of the neural network classification model classification layer, W L-1 T represents transposition of W L-1 , ∥W E ∥ 2,1 represents an L21 norm of W E , Ω represents a positive semi-definite symmetric matrix used to represent the semantic relationship, and an initial value of Ω is an identity matrix.

4 . The method according to claim 3 , wherein the obtaining the weight matrix of the neural network classification model fusion layer and the weight matrix of the neural network classification model classification layer by optimizing the target function comprises:
obtaining the weight matrix of the neural network classification model fusion layer and the weight matrix of the neural network classification model classification layer by optimizing the target function by using a proximal gradient method.

5 . The method according to claim 4 , wherein the optimizing the target function by using the proximal gradient method comprises:
initializing the weight matrix of the neural network classification model fusion layer and the weight matrix of the neural network classification model classification layer that are in the target function; obtaining a deviation between an output predictor and an actual value by inputting the features of the video samples; and adjusting the weight matrix of the neural network classification model fusion layer and the weight matrix of the neural network classification model classification layer according to the deviation until the deviation is less than a preset threshold.

6 . A video classification apparatus, comprising:
a processor; and a memory coupled to the processor, where the memory stores processor-executable instructions which when executed causes the processor to implement operations including: establishing a neural network classification model according to a relationship between features of video samples and a semantic relationship of the video samples; obtaining a feature combination of a to-be-classified video file; and the to-be-classified video file by using the neural network classification model and the feature combination of the to-be-classified video file.

7 . The apparatus according to claim 6 , wherein the operations further include: obtaining a weight matrix of a neural network classification model fusion layer and a weight matrix of a neural network classification model classification layer according to the relationship between the features of the video samples and the semantic relationship of the video samples; and establishing the neural network classification model according to the weight matrix of the neural network classification model fusion layer and the weight matrix of the neural network classification model classification layer.

8 . The apparatus according to claim 7 , wherein the operations further include: obtaining the weight matrix of the neural network

Assignees

• Huawei Tech Co Ltd
• Univ Fudan

Inventors

• Jiang Yugang
• Wu Zuxuan
• Xue Xiangyang
• Gu Zichen
• Chai Zhenhua

Classifications

• G06V10/806

  of extracted features · CPC title

• G06V10/82 Primary

  using neural networks · CPC title

• G06F18/24133

  Distances to prototypes · CPC title

• G06F18/253

  of extracted features · CPC title

• G06F18/22

  Matching criteria, e.g. proximity measures · CPC title