A Method for Global Color Contrast Detection and Segmentation of Saliency Maps

Abstract

The invention belongs to the technical field of color image processing, in particular to a method for global color contrast detection and saliency map segmentation. The method of the invention includes saliency detection and saliency map segmentation. The saliency detection adopts the global color contrast method, and the saliency map segmentation adopts the dynamic threshold value method. The present invention first obtains preliminary saliency maps by extracting the features of the global color contrast, and then uses color dispersion and spatial information to further improve their saliency; using the above-mentioned generated saliency maps as initial values, through iteratively dynamically updated thresholds, to segment multiple salient objects. The experimental results on the public Achanta database show that the performance of the present invention on the ROC curve is better than that of the methods reported in the literature, and it is suitable for multi-target detection and segmentation problems.

Description

technical field [0001] The invention belongs to the technical field of color image processing, and in particular relates to a salient detection and segmentation method of global color contrast. Background technique [0002] At any one time, there are a large number of visual stimuli in the environment. After a long evolution, the Human Visual System (HVS) can quickly extract objects and regions of interest from complex environments to reduce the complexity of visual signal processing. These local areas that attract visual attention are usually called saliency regions. The image obtained after normalizing the saliency of each area in the scene is called a saliency map. [1] . [0003] In image processing and computer vision, a robust and accurate salient region automatic detection algorithm has important practical value. This is because from the perspective of computational complexity, some image processing tasks often cannot process all the visual information in the visua...

AI Technical Summary

Problems solved by technology

The main disadvantages of these existing reported algorithms are: algorithms based on local contrast class usually calculate the saliency by detecting the edge of the image, so only the edge of the target can produce high saliency; while some other algorithms can only detect Global maximum saliency, so it is not suitable for multi-target detection problems; algorithms that ignore the spatial relationship of parts of the image do not effectively use spatial information, etc.

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