LONG Peng, LU Huaxiang, WANG An. A Novel Unsupervised Two-Stage Technique in Color Image Segmentation[J]. Chinese Journal of Electronics, 2018, 27(2): 405-412. doi: 10.1049/cje.2018.01.011
Citation: LONG Peng, LU Huaxiang, WANG An. A Novel Unsupervised Two-Stage Technique in Color Image Segmentation[J]. Chinese Journal of Electronics, 2018, 27(2): 405-412. doi: 10.1049/cje.2018.01.011

A Novel Unsupervised Two-Stage Technique in Color Image Segmentation

doi: 10.1049/cje.2018.01.011
Funds:  This work is supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (No.XDB02080002), and the National Natural Foundation of China for Young Scientist (No.61401423).
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  • Corresponding author: LONG Peng (corresponding author) received the B.S. degree in Electronic of Science and Technology from Huazhong University of Science and Technology in 2012, and is currently working toward the Ph.D. degree in the Institute of Semiconductors, Chinese Academy of Sciences (CAS). His current research interests include the image processing and pattern recognition. (Email:longpeng@semi.ac.cn)
  • Received Date: 2015-03-09
  • Rev Recd Date: 2016-01-19
  • Publish Date: 2018-03-10
  • 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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