Visual attention computing method and system based on bit entropy rate

Abstract

The invention discloses a visual attention computing method and a system based on bit entropy rate. The method includes that: a sparse codebook is learned to obtain a sparse codebook primary function; the sparse codebook primary function is adopted to filter the image or video data and obtain a plurality of sub-band feature graphs; a corresponding fully-connected graph is established for each sub-band feature graph; the information transfer method of each fully-connected graph adopts the random walk method, during the random walk process, the significance measurement is performed according tothe bit entropy rate to accordingly obtain a bit entropy rate graph corresponding to each sub-band feature graph; the bit entropy rate graphs are added together to obtain the significance graph of the image or video data. As indicated in large amount of experiments, and compared with other methods in prior art, the analysis result of image significance analysis or video significance analysis obtained through the invention is more accurate and is supported by the basis of physiology and psychology.

Description

technical field [0001] The present invention relates to multiple fields such as image and video processing technology, computer vision and visual perception, and in particular to a visual attention calculation method and system based on bit entropy rate. Background technique [0002] Selective attention means that psychological resources are selectively allocated to certain cognitive processes, making these cognitive processes process information more quickly and accurately. Attention is important for coordinating various cognitive processes. Human beings receive a large amount of external information every moment, and are in a state of being "information bombarded". It is impossible for our limited psychological resources and neural resources to process so much information at the same time. We can only selectively process high-priority information and ignore low-priority information. The role of attention is reflected in this. [0003] Selective attention is a very comple...

AI Technical Summary

Problems solved by technology

[0005] There are two problems in the existing methods: on the one hand, some of the existing methods are based on the center-surrounding model and some are based on the principle of information maximization, and few methods can be explained from both aspects at the same time; on the other hand, the existing methods The method of the method is not particularly accurate in the prediction of visual attention points, and there is a big gap with the real eye movement data.

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