Color image segmentation algorithm based on improved convex hull and color contrast

doi:10.3969/j.issn.1000-1565.2018.05.015

Abstract

Abstract: The detection method based on feature contrast is easy to misjudge the background area with high contrast as the target when processing complex background images. This may lead to inaccurate segmentation results. In order to solve this problem, this paper presents a new method for color image segmentation based on improved convex hull and color contrast. Firstly, the method takes superpixel as the basic processing unit, and measures the saliency map based on color contrast with uniqueness and spatial distribution. Secondly, it calculates convex hull using color enhancement Harris corner points. The convex hull is corrected through FH algorithm. The center saliency map can be achieved through the corrected convex hull. The final saliency map is obtained by the fusion of the contrast map and the center saliency map. Finally, the object was segmented out using OTSU method. Simulation experiments on the MSRA 1 000 and ECSSD databases were conducted. The results indicate that the proposed algorithm performs better in visualization, precision.

Key words: color image segmentation, color contrast, superpixel, convex hull, saliency map

CLC Number:

TP319

CHEN Liping, ZHOU Hang,ZHANG Ningyu, YANG Wenzhu, CUI Zhenchao, LIU Qing. Color image segmentation algorithm based on improved convex hull and color contrast[J]. Journal of Hebei University (Natural Science Edition), 2018, 38(5): 543-548.

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