Robust color image segmentation by Karhunen-Loeve transform based Otsu multi-thresholding and K-Means Clustering

In this paper, a novel fast approach is proposed to achieve image segmentation in color image. This method helps to refine the foreground regions and achieves the goal of robust color image segmentation throw the following four steps. First, modified Karhunen-Loeve transform is performed to reduce the redundant component, thus selecting the most important part of the color images. Second, a multi-threshold Otsu method is carried out to select the best thresholds from image histogram. Thereby, the conventional Otsu method has been extended from gray level to color level. Third, improved Sobel edge detection is added to enhance the weight of edge detail of the foreground image. Finally, a K-Means Clustering is used to merge the over-segmented regions. Experimental results prove that this method has a good performance even when the color image has a complicated structure in the background.