Study On Color Texture Image Segmentation Based On Graph Theory

The image segmentation is an important branch in the image processing field, its result is very importation to the image analysing and understanding. The texture is the universal characteristic in the image and it is difficulty to describe. The texture in the image reflects a certain variety in the object surface color and gray scale according to itself attribute. So the texture becomes a kind of obvious feature to describe object surface with different object on the different scale, and it occupies an important position in the image segmentation. The texture segmentation also becomes an important direction being highly valued with a lot of domestic and foreign researchers, and it has got the extensive application in the actual.The paper is studys the algorithm of color-texture segmentation, and generalizes some texture segmentation algorithm used widely at present, then research how to effectively extract texture feature of image and apply it to texture segmentation, try to find relizble, stable and pratical texture segmentation algorithm.The color-texture segmentation is divided into the color-texture feature extraction and consistency segmentation based on feature vector. The paper is to research around the above two points.The paper at first discusses the meaning of texture research, and summarize consensus by analyzing and comparing the typical texture definitions mothod. Filter bank texture feature extraction mothod has an advantage that more suitable with human brain recognition system and eye vision systems, so the paper presents a set of filter to extract texture feature of image. And the HSI color space is chosen to extract color feature of image, the combination of texture and color feature vector is used to format color-texture feature vector, which is applied to color-texture image feature exaction.At the same time, the texture feature of corresponding image pixel neighbors in this paper is described by texton histograms, and the pairwise texture similarity is constructed by comparing windowed texton histograms. The neighbors selection of texton histograms based on the median of corresponding pixel distance in the textons channel is present, and it is used to compute windowed texton histograms of given pixel neighbors with different scale texture.According to the widely mathematical foundation and the capability of capturing global features, the Normalized Cut framework is used to segment color-texture image. For the problem of slowly running speed and being unable to deal with high-resolution image, the effect of graph connection radius on segmentation results and the deciding factor of running time are analyzed. A rapid Normalized Cut is designed to compress large image graph into multiple scales graph with linear-time complexity. In the multiscale partition criterion, the cross-scale constraint matrix is defined for multiscale segmentation, considering the benefits of detailed boundary or fine level segmentation and clearing region of coarse level segmentation. The results show, multiscale Normalized Cut markedly reduces the running time of original Normalized Cut and segments high-resolution image, which provide the new idea for solving the practicality of Normalized Cut framework.Aiming at the specific form of color-texture feature, the multiscale Normalized Cut is extended to the wavelet domain. In the algorithm, the color-texture feature is used to describe color-texture and texton histogram is used to statistic feature vector which can embody the regional property of texture. On the other hand, Normalized Cut criterion is used to segment statistic information which assures the segmentation can capture global feature of image. And the multiscale graph in the wavelet domain can assure rapid running speed. Experimental results show that the multiscale Normalized Cut in the wavelet domain can assure the rapid running time and high-resolution, at the same time can gain accurate segmentation result. At the last, the algorithm is applied to remote-image segmentation.