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A Color- and Texture-based Image Segmentation Algorithm

Wang X. Y., Sun Y. F.

Machine Graphics and Vision

2010

Vol. 19, No. 1

3-18

Image segmentation is a classic inverse problem which consists in obtaining a compact, region-based description of the image scene by decomposing it into meaningful or spatially coherent regions sharing similar attributes. Because a color image can provide more perceptual information, color image segmentation is being paid more and more attention. In this paper, we propose a new approach to color image segmentation that is based on low-level features of color and texture. The approach is aimed at segmentation of natural scenes where the color and texture of each segment do not typically exhibit uniform statistical characteristics. Firstly, local color composition is described in terms of spatially adaptive dominant colors by using the Gibbs random field, and the color image is segmented into regions according to the local color composition. Secondly, the texture characteristics of the grayscale component are described by utilizing the Steerable filter, and the grayscale component of color image is cut into flat regions and non-flat regions. Thirdly, the local color composition and texture characteristics are combined to obtain an overall crude segmentation. Finally, an elaborate border refinement procedure is used to obtain accurate and precise border localization by appropriately combining color-texture features with the Normalized Cuts. The experimental results demonstrate that the color image segmentation results of the proposed approach exhibit favorable consistency in terms of human perception.