Object significance detecting method based on color contrast and color distribution

Abstract

The invention provides an object significance detecting method based on color contrast and color distribution. The steps of the object significance detecting method based on the color contrast and the color distribution include that S1: an input image is divided into small size super-pixels, average color and position in the super-pixels are calculated; S2: the center-periphery color contrast of each super-pixel is calculated, the color contrast value is multiplied by a priori distribution, and at last color contrast significance diagram is obtained by using a significance smooth operation; S3: color distribution variance of each super-pixel is calculated, and thereby a color distribution significance diagram is obtained; S4: the color distribution significance diagrams obtained by the S2 and the S3 are multiplied and refined by using MeanShift division, edges of an object are enabled to be more fine, and the final significance diagram is output. According to the object significance detecting method based on the color contrast and the color distribution, the significance diagram obtained can evenly highlight the significant object in the significance diagram, the edge details of the object are well retained, the background interference is restrained, and the following up processes such as the target object division are benefited.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an object saliency detection method based on image color contrast and color distribution. Background technique [0002] Research on the human visual selective attention mechanism has always been a topic of great interest, because this visual selection mechanism allows us to focus various computing resources on the most valuable information. Detecting salient objects in images is an important aspect in this field, which has a wide range of applications in automatic object localization and segmentation, image retrieval, and content-based image scaling. The result of the saliency detection is called the "saliency map", and the brighter the area on the map, the easier it is to attract people's attention. Saliency detection methods can generally be divided into two categories: bottom-up and top-down. The bottom-up saliency detection method simulates the human instin...

AI Technical Summary

Problems solved by technology

Since this method only considers the color contrast, its final saliency map may contain a lot of background interference, and it cannot obtain satisfactory detection results on images with complex background textures.

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