Kernel-based clustering of image pixels with modified Differential Evolution

A modified differential evolution (DE) algorithm is presented for clustering the pixels of an image in its intensity space. The algorithm requires no prior information about the number of naturally occurring clusters in the image. It employs a kernel-induced similarity measure instead of the conventional sum-of-squares distance. Use of the kernel function makes it possible to partition data that is linearly non-separable and non hyper-spherical in the original input space, into homogeneous groups in a transformed high-dimensional feature space. A novel chromosome representation scheme is adopted for selecting the optimal number of clusters from several possible choices. Extensive performance comparison over a test-suit of five gray scale images (with ground truth) indicates that the proposed algorithm has an edge over a few state-of-the-art algorithms for automatic multi-class image segmentation.