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Video semantic analysis method based on self-adaption probability hypergraph and semi-supervised learning

A semi-supervised learning and self-adaptive technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as inability to achieve self-adaptation and large amount of calculation

Inactive Publication Date: 2014-03-26
JIANGSU UNIV
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Problems solved by technology

Although this method can adaptively determine a better radius parameter, the selection range of parameters is still a limited number given artificially, and the amount of calculation is large, so it cannot be completely adaptive.

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  • Video semantic analysis method based on self-adaption probability hypergraph and semi-supervised learning
  • Video semantic analysis method based on self-adaption probability hypergraph and semi-supervised learning
  • Video semantic analysis method based on self-adaption probability hypergraph and semi-supervised learning

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Embodiment Construction

[0053] In order to better understand the technical content of the present invention, specific embodiments are given together with the attached drawings for description as follows.

[0054] refer to figure 1 , figure 2 and image 3 As shown, according to a preferred embodiment of the present invention, the video semantic analysis method based on adaptive probability hypergraph and semi-supervised incremental learning comprises the following steps: S1: adopt the construction method of adaptive probability hypergraph to build a hypergraph model; S2 : Semi-supervised learning of hypergraph model using spectral graph segmentation principle; S3: Using incremental mechanism to improve semi-supervised model based on adaptive probabilistic hypergraph; and S4: Using the improved hypergraph model to test the semantics of video for analysis.

[0055] refer to figure 1 , in the aforementioned hypergraph construction process, first define an adaptive threshold function, if the value of...

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Abstract

The invention provides a video semantic analysis method based on a self-adaption probability hypergraph and incremental semi-supervised learning. The video semantic analysis method based on the self-adaption probability hypergraph and the semi-supervised learning comprises the steps that (S1) a hypergraph model is established by means of a self-adaption probability hypergraph establishment method, (S2) the semi-supervised learning is conducted on the hypergraph model by means of the spectrogram segmenting principle, (S3) a semi-supervised model based on the self-adaption probability hypergraph is perfected by means of an increment mechanism, and (S4) semantic analysis is conducted on a tested video by means of the perfected hypergraph model. According to the video semantic analysis method based on the self-adaption probability hypergraph and the semi-supervised learning, the establishment of the self-adaption probability hypergraph and an incremental semi-supervised learning method are combined for use, the sensibility to a radium parameter when an ordinary hypergraph model is established is eliminated, and the accuracy and the robustness of the model are improved; in addition, under the incremental semi-supervised learning mechanism, semantic searching accuracy and semantic searching completeness are improved remarkably.

Description

technical field [0001] The invention relates to the technical field of video semantic detection, in particular to a video semantic analysis method based on an adaptive probability hypergraph and an incremental semi-supervised learning model. Background technique [0002] In order to achieve multi-semantic learning of complex videos, a hypergraph model is proposed to describe the correlation information between multiple semantic concepts of complex videos. Experiments have proved that the hypergraph model can well complete various clustering and classification tasks. However, one of the shortcomings of this kind of hypergraph model is that it treats all vertices in the hyperedge equally, and ignores the differences between these vertices, which will lead to the loss of some information, which may cause certain damage to the analysis and detection results of video semantics. Impact. In addition, the construction method of the model is sensitive to the radius parameter k in th...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/7847
Inventor 詹永照孙佳瑶毛启容牛德姣
Owner JIANGSU UNIV
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