| || Split-and-merge Procedure for Image Segmentation using Bimodality Detection Approach
Author : Chaudhuri, D.;Agrawal, A.
Source : Defence Science Journal ; Vol:60(3) ; 2010 ; pp 290-301
Subject : 62 Engineering;Defence Science Journal
Keywords : Segmentation;clustering;bimodality;minimal spanning-tree;homogeneity factor;split-and-merge technique;image segmentation
Abstract : Image segmentation, the division of a multi-dimensional image into groups of associated pixels, is an essential step for many advanced imaging applications. Image segmentation can be performed by recursively splitting the whole image or by merging together a large number of minute regions until a specified condition is satisfied. The split-and-merge procedure of image segmentation takes an intermediate level in an image description as the starting cutest, and thereby achieves a compromise between merging small primitive regions and recursively splitting the whole images to reach the desired final cutest. The proposed segmentation approach is a split-andmerge technique. The conventional split-and-merge algorithm is lacking in adaptability to the image semantics because of its stiff quadtree-based structure. In this paper, an automatic thresholding technique based on bimodality detection approach with non-homogeneity criterion is employed in the splitting phase of the split-and-merge segmentation scheme to directly reflect the image semantics to the image segmentation results. Since the proposed splitting technique depends upon homogeneity factor, some of the split regions may or may not split properly. There should be rechecking through merging technique between the two adjacent regions to overcome the drawback of the splitting technique. A sequential-arrange-based or a minimal spanning-tree based approach, that depends on data dimensionality of the weighted centroids of all split regions for finding the pair wise adjacent regions, is introduced. Finally, to overcome the problems caused by the splitting technique, a novel merging technique based on the density ratio of the adjacent pair regions is proposed. The algorithm has been tested on several synthetic as well as real life data and the results show the efficiency of the segmentation technique.