Textural feature and color feature fusion-based image saliency detection method

Abstract

The invention discloses a textural feature and color feature fusion-based image saliency detection method, and mainly aims at solving the problem that the textural feature utilization is insufficient and the high-texture image saliency detection result is relatively bad in the prior art. The scheme is that the method comprises the following steps of: 1) inputting an image and removing textures of the image by utilizing a total variation model-based filtering method so as to obtain an image with color textures; 2) filtering the input image by utilizing a Garbor filter so as to obtain an image with image texture information; 3) calculating a preliminary contrast value according to the image with the color features; 4) calculating a background probability according to the image with the texture information; and 5) fusing the preliminary contrast value and the background probability to obtain a new contrast and then obtaining a saliency map on the basis of color and texture features. According to the method disclosed by the invention, the color and texture information of the image is sufficiently utilized, so that the complicated-texture image detection effect is improved; and the method can be applied to computer vision tasks.

Description

technical field [0001] The invention belongs to the technical field of image detection, and in particular relates to an image saliency detection method, which can be used for image segmentation, target recognition, adaptive image compression, content-aware image scaling and image retrieval. Background technique [0002] People often effortlessly judge the importance of image regions and focus on the important parts. Since computing resources in image analysis can be allocated optimally through salient regions, it is of great significance to detect salient regions of images by computer. Extracting saliency maps is widely used in many computer vision applications, including image segmentation of objects of interest, object recognition, adaptive image compression, content-aware image scaling, and image retrieval. [0003] Saliency stems from visual uniqueness, unpredictability, scarcity, and singularity, and it is often attributed to variations in image properties such as colo...

AI Technical Summary

Problems solved by technology

[0006] All the methods mentioned above have a common problem: the texture information of the image is not considered, and the texture information is an important feature reflecting the intrinsic nature of the image

Castleman et al. believe that texture is the spatial distribution attribute of the gray level of pixels in an image. The inherent attribute of this spatial structure can be described by the correlation between neighboring pixels. The visual features that reflect the homogeneous phenomenon in the image reflect the intrinsic properties of the object surface. Due to the lack of texture features, the above methods will inevitably lead to a decline in the quality of high-textured images.

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