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Micro-video popularity prediction method based on attributive classification and multi-angle feature fusion

A technology of attribute classification and feature fusion, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as inability to meet various needs, inability to fully understand feature asymmetry, etc., and achieve the effect of improving accuracy

Inactive Publication Date: 2018-01-19
TIANJIN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But current methods such as TMALL [1] etc., cannot fully understand the inequalities between features, treat all features equally, and cannot meet various needs in practical applications

Method used

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  • Micro-video popularity prediction method based on attributive classification and multi-angle feature fusion
  • Micro-video popularity prediction method based on attributive classification and multi-angle feature fusion
  • Micro-video popularity prediction method based on attributive classification and multi-angle feature fusion

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0026] In order to achieve a better prediction effect, a comprehensive, automatic and accurate method for predicting the popularity of micro-videos is needed. Research shows that micro-videos with similar features have similar popularity. The embodiment of the present invention proposes a micro-video popularity prediction method based on attribute classification and multi-view feature fusion, see figure 1 , see the description below:

[0027] 101: Using social attribute features to classify micro-videos, assigning micro-videos to different popularity levels, and obtaining the primary popularity range of micro-videos;

[0028] Among them, the classification of popularity levels is related to the popularity scores of the micro-videos in the test set. Arrange the scores of the micro-videos in the training set from high to low, and then distribute the micro-videos to different levels evenly from high to low in popularity scores, and classify this as the popularity level of the t...

Embodiment 2

[0041] The scheme in embodiment 1 is further introduced below in conjunction with specific calculation formulas and examples, see the following description for details:

[0042] 201: Extract 4 kinds of view mode features for a given micro-video, namely: visual features, acoustic features, text features and social attribute features;

[0043] The embodiment of the present invention firstly extracts four common features of micro-video research from a given micro-video, including: visual features, acoustic features, text features and social attribute features.

[0044] 1. Visual features include: color histogram information, object information in the micro-video (can be obtained by convolutional neural network or other methods, and the embodiment of the present invention does not limit this) and aesthetic features.

[0045] 2. Acoustic features include: the music in the micro-video and the features of other main background sounds.

[0046] 3. Text features include: text annotati...

Embodiment 3

[0073] Below in conjunction with concrete experimental data, example, the scheme in embodiment 1 and 2 is carried out feasibility verification, see the following description for details:

[0074] The test data set used in this experiment is a micro video set downloaded from the Vine social networking site (well known to those skilled in the art, the embodiment of the present invention will not repeat this), and the length of the micro video is 6S. The mean square error and Spearman rank correlation value are used to measure the micro-video popularity prediction performance of this method, the mean square error (nMSE) represents the absolute accuracy of prediction, and the Spearman rank correlation value (SRC) represents the ranking accuracy of prediction .

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Abstract

The invention discloses a micro-video popularity prediction method based on attributive classification and multi-angle feature fusion, and the method comprises the following steps: carrying out the classification of a micro-video through social attribute features, distributing the micro-video to different popularity levels, and obtaining a primary popularity range of the micro-video; calculating the similarity relation of the micro-video at each popularity level at different modal view angles, and describing the similarity relation through a Laplacian matrix; taking the linear combination of Laplacian matrixes in different modes as the Laplacian matrix of a public subspace; and carrying out the prediction of the popularity of the micro-video based on the Laplacian matrix through a semi-supervised method. According to the invention, the method achieves the learning through the attributive classification and multi-angle feature fusion, eliminates the restrictions on the popularity prediction from the single view angle feature, and emphasizes the decisive effect of the social attribute features in the popularity prediction.

Description

technical field [0001] The invention relates to the field of micro-video popularity, in particular to a micro-video popularity prediction method based on attribute classification and multi-view feature fusion. Background technique [0002] With the popularity of network technology and social platforms, micro-video has received more and more attention as a new user content. Micro-videos refer to short video clips ranging from 30 seconds to 20 minutes in length. The emergence of micro-videos not only conforms to the online viewing habits and mobile terminal characteristics under the fast-paced lifestyle of modern society, but also meets the needs of consumers' independent participation and return on attention in the era of entertainment explosion and attention scarcity. It is foreseeable that " What "micro video" brings to the public will be free video enjoyment anytime, anywhere. The prediction of micro-video popularity has a guiding role in advertising push, video recommen...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 苏育挺白须张静
Owner TIANJIN UNIV