Short video automatic labeling method based on feature and multi-label enhanced representation

An automatic labeling and short video technology, applied in the field of short video, can solve problems such as rising labor costs, low efficiency, and complicated processes, and achieve the effect of improving accuracy

Pending Publication Date: 2020-09-29
TIANJIN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Using manual labeling will make the process more complicated, resulting in low efficiency and increased labor costs.

Method used

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  • Short video automatic labeling method based on feature and multi-label enhanced representation
  • Short video automatic labeling method based on feature and multi-label enhanced representation
  • Short video automatic labeling method based on feature and multi-label enhanced representation

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Embodiment 1

[0029] The embodiment of the present invention provides a short video automatic labeling method based on feature and multi-label enhanced representation, see figure 1 , the method includes the following steps:

[0030] 101: Reconstruct the original feature matrix by using the dictionary mapping matrix, the product of the public low-rank representation and the sparse error matrix, so as to form a multi-view low-rank representation item as a whole (the first objective function);

[0031] 102: By clustering the overall data set, obtain all data sets and potential label correlation information in different clusters, and form a global and local label correlation learning item (second objective function);

[0032] 103: Use the public low-rank representation as the predicted label, subtract it from the real label to obtain the labeling error and minimize it, forming a minimum labeling error term (the third objective function);

[0033] 104: Obtain the total objective function weight...

Embodiment 2

[0039] The scheme in embodiment 1 is further introduced below in conjunction with calculation formula and examples, see the following description for details:

[0040] 201: Use the penultimate fully connected layer of the VGG-m-2048 network to extract 2048-dimensional advanced visual semantic features, use the TDD model and Fisher Vector to extract 2048-dimensional trajectory features from the video dataset, and use the l2 norm for each The characteristics of the perspective are standardized to obtain the final standardized multi-view feature X 1 and x 2 .

[0041] 202: Determine the characteristics X of different viewing angles i The public low-rank representation of L, according to the formula:

[0042] x i =D i L+E i ,i=1,2,...,V

[0043] (1)

[0044] Among them, using the dictionary mapping matrix D i feature X from different perspectives i Mapping to the public low-rank representation L, and making the rank of L reach the lowest, the rank of the matrix can be re...

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Abstract

The invention discloses a short video automatic labeling method based on feature and multi-label enhanced representation, which comprises the following steps: reconstructing an original feature matrixby using a dictionary mapping matrix, a product of public low-rank representation and a sparse error matrix to form a multi-view low-rank representation item; clustering an overall data set to obtainall data sets and potential label correlation information in different clusters to form global and local label correlation learning items; taking the common low-rank representation as a prediction label, subtracting the prediction label from a real label to obtain a labeling error, and minimizing the labeling error to form a minimized labeling error term; and weighting the multi-view low-rank representation item, the global and local label correlation learning item and the minimized labeling error item to obtain a total target function, optimizing the total target function by using an alternating direction multiplier method, introducing a Lagrange multiplier, and sequentially iteratively updating each matrix variable until the value of the target function converges to obtain a final labeling result. According to the method, the accuracy in the short video multi-label labeling problem is improved.

Description

technical field [0001] The present invention relates to the field of short videos, in particular to a short video automatic labeling method based on feature and multi-label enhanced representation. Background technique [0002] At present, with the changes in the working and living environment and the rapid popularization of mobile phone networks, people's way of receiving information has gradually changed from traditional long-term reception to fragmented reception. People are no longer limited to simply reading and creating for a long time, but obtain the information they want in a short period of time through mobile phone networks and other means at any time. In this context, short videos emerged as the times require. [0003] As an emerging media form, short videos are widely spread on major online social platforms, and have achieved rapid development in recent years. Major short video network platforms allow users to make, process, and upload short videos that are ofte...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/783G06F16/75
CPCG06F16/783G06F16/75Y02T10/40
Inventor 吕卫李德盛井佩光苏育挺
Owner TIANJIN UNIV
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