A novel approach for color image segmentation based on region growing

Abstract

In this paper, a novel approach for obtaining all possible uniform regions in the color image is proposed. The proposed approach integrates a color edge detection method; image partitioning; the initial seeds and thresholding region growing; the average overlap metric (AOM) and voting algorithms. It starts by decomposing the color image into less complicated component images. The edges of the color image are detected to extract the non-edge pixels during the region growing processes. Then, a source image is partitioned into cells while seeds are obtained by applying the local search algorithm in the image histogram. The growing processes are improved by color image thresholding algorithm which is necessary for finding the homogeneity criterion to merge similar pixels. The seeds and the homogeneity criterion values are the input to the region growing method to segment an image into regions; some of them are overlapped or been redundant. The AOM algorithm is applied to classify the redundant regions based on pixel similarity. These regions are fed to voting technique in order to produce region of points whose have similar values to utilize the compactness of the clusters forming these uniform regions. Experimental results are conducted using different color images with different sizes. Moreover, the proposed method is experimented by different noisy images and is compared with the well-known existing methods to prove its efficiency. The obtained results reveal the accuracy and stability of proposed technique and its superiority over other three well-known existing methods.