Visual feature segmentation semantic detection method and system in video description

Abstract

The invention discloses a visual feature segmentation semantic detection method and system in video description. Segmenting the visual feature into a plurality of visual segmentation features representing local information; local semantic information is extracted through a multi-layer perceptron, and semantic information with global and local double expressions is obtained after global semantic features are fused; thus, the representation capability of semantic features is enhanced; the obtained semantic features are applied to video description tasks, the precision of a video description model is improved, an accurate video text description result is obtained; therefore, the method can be well applied to the fields of security monitoring, short video content review and the like.

Description

technical field

[0001] The invention relates to the technical field of deep learning video understanding, in particular to a semantic detection method and system for visual feature segmentation in video description. Background technique

[0002] With the rapid development of information technology, security monitoring equipment is used more and more widely, and with the emergence of a large number of short video platforms, monitoring and automatic review of short video content has become one of the current research hotspots. At present, the censorship of video content mainly relies on manual means, and the computer automatic censorship technology is not mature enough, which cannot fully realize and understand the video content.

[0003] Existing video description algorithms increasingly use video semantic features as auxiliary features, and use them together with visual information as encoding features to output corresponding text descriptions in long short-term memory netwo...

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