Cooperative significance detection method based on superpixel clustering

Abstract

The invention discloses a cooperative significance detection method based on superpixel clustering, and the method comprises the steps: constructing a superpixel pyramid, and replacing a common pixel through a superpixel block; speeding up the cooperative significance calculation; constructing the superpixel pyramid, so the feature information at different scale can be obtained, and the boundary accuracy of a cooperative significance target can be guaranteed. Based on the above, a clustering method can achieve the further classification of superpixel blocks, ad further speeds up the calculation of the cooperative significance. Finally, a method of fusion of a cooperative significance map and a saliency map is used for obtaining a final cooperative significance map, thereby guaranteeing the accuracy of the cooperative significance target. The boundary contour of the obtained cooperative significance target is more accurate in location, and the method has advantages in time and accuracy.

Description

technical field [0001] The invention relates to an image detection and processing method, in particular to an image collaborative saliency detection method, which is used to detect common salient regions in multiple images. Background technique [0002] Saliency detection is to quickly detect objects of interest in images or videos by simulating the visual attention mechanism of the human eye, and the purpose of collaborative saliency detection is to detect the same or similar salient regions in multiple images or videos. It has wide application value in many fields, such as collaborative segmentation, video foreground detection, image retrieval and object tracking, etc. In recent years, with the rapid development of Internet and multimedia technologies, it has gradually become a new demand to find the same or similar saliency detection technology from multiple images or videos. Image dominance allows for better suppression of the salient background or noise in individual i...

AI Technical Summary

Problems solved by technology

However, current superpixel segmentation methods combined into collaborative saliency detection are not content-aware and do not perform superpixel segmentation at multiple scales, so the boundary contours of salient objects are difficult to locate accurately

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