Area-of-interest detection method based on background prior and foreground node

Abstract

The invention discloses an area-of-interest detection method based on background prior and a foreground node. The method comprises steps of 1) by use of SLIC algorithm, segmenting an original image into super-pixels; 2) by use of K-means clustering algorithm, carrying out clustering on boundary super-pixels, according to a clustering result, constructing a global color difference matrix and a global space distance matrix, fusing the global color difference matrix and the global space distance matrix into a saliency map based on background prior, and finally, by use of a single-layer cellular automaton, primarily optimizing the saliency map based on the background prior; 3) carrying out adaptive threshold segmentation on the saliency map based on the background prior so as to obtain a foreground node, according to a contrast ratio relation, obtaining a saliency map based on the foreground node, and by use of the biased Gauss filtering, carrying out optimization; and 4) fusing the saliency map based on the background prior and the saliency map based on the foreground node, obtaining the final saliency map. According to the invention, the method is used in an image processing processand can be widely applied in visual working field like visual tracking, image segmentation and target re-positioning.

Description

technical field [0001] The invention relates to a method for detecting a region of interest based on background prior and foreground nodes. The method can detect regions of interest different from the background contrast, background complexity and images of regions of interest with different areas. As a result, the present invention, as an image preprocessing process, can be widely applied to visual work fields such as visual tracking, image classification, image segmentation, and target relocation. Background technique [0002] With the rapid development and promotion of information technology, image data has become one of the important sources of information for human beings. The amount of information received by people is increasing exponentially. How to select the target area of ​​human interest from the massive image information is of great research significance. . Studies have found that in complex scenes, the human visual processing system will focus visual attention...

AI Technical Summary

Problems solved by technology

However, it is unreasonable to classify all pixels on the boundary as the background based on the background-first method. If the target object appears on the edge, it will directly lead to a large deviation in the result; in addition, only using boundary information also has certain limitations.

[0004] At present, the main problem of ROI detection is that the ROI is not prominent, and the background noise cannot be well suppressed.

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