Segmentation of color images using EM Cost with spatial refinement algorithm on MBWT Features

Abstract

This paper proposes a novel technique to segment the color images combining M-Band Wavelet transform(MBWT) and Expectation Maximization (EM) with cost spatial refinement algorithm. One of the drawbacks of standard wavelets is that they are not suitable for the analysis of high frequency signals with relatively narrow bandwidth. This drawback has been overcome using MBWT. Also M-band wavelet decomposition yields a large number of sub bands which is required for improving the performance accuracy. The proposed algorithm first decomposes the input image into sixteen subimages by applying MBWT. Then, median feature is computed for each subimage and maximum energy subimage is chosen as the appropriate feature space on which EM with cost spatial refinement algorithm is applied. This new combined algorithm produces very good segmentation results by taking advantage of M-Band Wavelet feature extraction and EM with cost spatial refinement algorithm. The segmentation result is more homogeneous and quite consistent with the visualized color distribution in the objects of the original images compared to Fuzzy C means and K means spatial refinement algorithms. Also EM with cost spatial refinement algorithm needs less computational time compared to other clustering algorithms.