Open AccessA Novel Unsupervised Two‐Stage Technique in Color Image Segmentation
Author(s) -
Long Peng,
Lu Huaxiang,
Wang An
Publication year - 2018
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2018.01.011
Subject(s) - artificial intelligence , stage (stratigraphy) , computer science , pattern recognition (psychology) , segmentation , computer vision , image (mathematics) , geology , paleontology
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