Automatic image pixel clustering with an improved differential evolution

This article proposes an evolutionary-fuzzy clustering algorithm for automatically grouping the pixels of an image into different homogeneous regions. The algorithm does not require a prior knowledge of the number of clusters. The fuzzy clustering task in the intensity space of an image is formulated as an optimization problem. An improved variant of the differential evolution (DE) algorithm has been used to determine the number of naturally occurring clusters in the image as well as to refine the cluster centers. We report extensive performance comparison among the new method, a recently developed genetic-fuzzy clustering technique and the classical fuzzy c-means algorithm over a test suite comprising ordinary grayscale images and remote sensing satellite images. Such comparisons reveal, in a statistically meaningful way, the superiority of the proposed technique in terms of speed, accuracy and robustness.