Color Image Segmentation Using Combined Information of Color and Texture

For the purpose of color image segmentation, an unsupervised peak value searching algorithm was proposed, which was used to determine the approximate dominant color components of image. First, the local peaks of 3D color histogram within the neighborhood of 3 times 3 times 3 were located. The corresponding color values of local peaks were regarded as initial clustering centers, and the number of local peaks were taken as the number of clustering. In addition, taking into account of the color difference induced by local illumination, the feature vector was constructed including color and texture features. Finally, K-means clustering algorithm was applied to segment the color image. Experiment results show that the proposed method can segment the color image accurately, corresponding with the human visual. Clustering number was determined adaptively, and the problem of over-segmentation was solved effectively. The segmentation result was benefit for the following steps in the computer vision.