High-texture image classification method and system based on visual saliency detection

Abstract

The invention discloses a high-texture image classification method and a system based on visual saliency detection, wherein, the method comprises the following steps of: segmenting a saliency region through an image pixel saliency value detection method of color contrast; the texture features and gradient features of salient regions are extracted by the complete local binary mode operator and thedirectional gradient histogram algorithm, and the image details are represented by the effective tandem fusion strategy. And classifying the extracted fusion vector by the nearest neighbor classifierto obtain the recognition rate. This method utilizes the visual attention model fitting human visual mechanism to locate the salient target region in the image, in order to get rid of the redundant background information, the high-texture image is segmented, and then the feature of the salient region is extracted and the image classification is completed, so that the different textures in the image (field of view) are automatically separated, which makes the subsequent scene description or target recognition possible.

Description

technical field [0001] The invention relates to the technical fields of image processing and computer vision, in particular to a method and system for classifying high texture images based on visual saliency detection. Background technique [0002] The essence of digital image processing is image texture analysis. In the intuitive experience of human visual system, the characteristics of texture mainly include roughness, directionality, contrast and regularity. Due to the complex types of natural textures, various shapes and complex structures, and the lack of understanding of the mechanism of texture perception by the human visual system, for digital image processing, how to extract numerical features that can effectively describe textures has always been an important issue in texture research. the key points and difficulties. Texture features can be roughly divided into four categories: statistics-based features, template convolution-based features, frequency-domain-based...

AI Technical Summary

Problems solved by technology

When a region contains multiple textures, the obtained feature will be a "four different images", so the error in dividing different textures in the same image by extracting texture features is relatively large

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