Image texture classification using a manifold-distance-based evolutionary clustering method

Abstract

We perform unsupervised image classification based on texture features by using a novel evolutionary clustering method, named manifold evolutionary clustering (MEC). In MEC, the clustering problem is considered from a combinatorial optimization viewpoint. Each individual is a sequence of real integers representing the cluster representatives. Each datum is assigned to a cluster representative according to a novel manifold-distance-based dissimilarity measure, which measures the geodesic distance along the manifold. After extracting texture features from an image, MEC determines partitioning of the feature vectors using evolutionary search. We apply MEC to solve seven benchmark clustering problems on artificial data sets, three artificial texture image classification problems, and two synthetic aperture radar image classification problems. The experimental results show that in terms of cluster quality and robustness, MEC outperforms the K-means algorithm, a modified K-means algorithm using the manifold-distance-based dissimilarity measure, and a genetic-algorithm-based clustering technique in partitioning most of the test problems.