A Novel Unsupervised Two-Stage Technique in Color Image Segmentation
A Novel Unsupervised Two-Stage Technique in Color Image Segmentation作者机构：Lab of Artificial Networks Institute of Semiconductors CAS CAS Center for Excellence in Brain Science and Intelligence Technology University of Chinese Academy of Sciences
出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))
年 卷 期：2018年第27卷第2期
页 面：405-412页
核心收录：
学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程]
基 金：supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB02080002) the National Natural Foundation of China for Young Scientist(No.61401423)
主 题：Color image segmentation Coarse segmentation Delicate segmentation Narrowband
摘 要：A new unsupervised two-stage method for color image segmentation is proposed. The method contains coarse segmentation and delicate segmentation. In coarse segmentation, we adaptively choose a gray channel from CIE-lab color space. The Otsu method combined with a refinement to its threshold is applied to get global optimal segmentation. In delicate segmentation, a narrowband based procedure is applied to get more accurate contour of the object and local optimal segmentation is achieved. Our method finally balance the global optimal and the local optimal. The proposed method does not need initial contours or initial labels, thus it is more robust in certain applications. Experimental results of our method in MSRA1000 database show that our method is robust in segmenting objects and backgrounds when possessing weakly heterogeneous color. Our method firstly achieves global optimal and then achieves local optimal which draws a new and prospective outlook for segmenting color images.
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