Visual saliency detection method with fusion of region color and HoG (histogram of oriented gradient) features

Abstract

The invention relates to a visual saliency detection method with fusion of region color and HoG (histogram of oriented gradient) features. At present, the existing method is generally based on a pure calculation model of the region color feature and is insensitive to salient difference of texture. The method disclosed by the invention comprises the following steps of: firstly calculating a color saliency value of each pixel by analyzing color contrast and distribution feature of a superpixel region on a CIELAB (CIE 1976 L*, a*, b*) space color component diagram of an original image; then extracting an HoG-based local rectangular region texture feature on an RGB (red, green and blue) space color component diagram of the original image, and calculating a texture saliency value of each pixel by analyzing texture contrast and distribution feature of a local rectangular region; and finally fusing the color saliency value and the texture saliency value of each pixel into a final saliency value of the pixel by adopting a secondary non-linear fusion method. According to the method disclosed by the invention, a full-resolution saliency image which is in line with sense of sight of human eyes can be obtained, and the distinguishing capability against a saliency object is further stronger.

Description

technical field [0001] The invention belongs to the field of computer vision, and specifically relates to a visual saliency detection method that fuses the features of regional color contrast and gradient direction histogram (the English abbreviation "HoG" is used hereinafter). Background technique [0002] Visual saliency is defined as the unpredictability and scarcity of vision. Supported by related theories of human visual attention, visual saliency models provide a fast and efficient approach as an important preprocessing mechanism in computer vision and image processing. [0003] Current research results show that most visual saliency models are based on a bottom-up process driven by underlying features. Among them, the far-reaching work is the feature fusion theory proposed by L. Itti et al. In their model, color, brightness, and orientation features are separately extracted from input images, annotated with geographic feature maps, and then synthesized using a linea...

AI Technical Summary

Problems solved by technology

These methods simulate human eye movement and track the focus of the eye. They have important research value in biology, but there are obvious deficiencies: low resolution, highlighted local outlines, and lack of complete regional awareness

However, the above methods are based on regional color and are not sensitive to significant differences in texture.

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