Computer Vision-Based Color Image Segmentation with Improved Kernel Clustering

Color image segmentation has been widely applied to diverse fields in the past decades for containing more information than gray ones, whose essence is a process of clustering according to the color of pixels. However, traditional clustering methods do not scale well with the number of data, which limits the ability of handling massive data effectively. We developed an improved kernel clustering algorithm for computing the different clusters of given color images in kernel-induced space for image segmentation. Compared to other popular algorithms, it has the competitive performances both on training time and accuracy. The experiments performed on both synthetic and real-world data sets demonstrate the validity of the proposed algorithm.