Wavelets-based clustering techniques for efficient color image segmentation

Abstract

This paper introduces efficient and fast algorithms for unsupervised image segmentation, using low-level features such as color and texture. The proposed approach is based on the clustering technique, using 1. Lab color space, and 2. the wavelet transformation technique. The input image is decomposed into two-dimensional Haar wavelets. The features vector, containing the information about the color and texture content for each pixel is extracted. These vectors are used as inputs for the k-means or fuzzy c-means clustering methods, for a segmented image whose regions are distinct from each other according to color and texture characteristics. Experimental result shows that the proposed method is more efficient and achieves high computational speed. © Springer International Publishing Switzerland 2014.