标题:Bhattacharyya distance-based irregular pyramid method for image segmentation
作者:Yu, Yuanlong; Gu, Jason; Wang, Junzheng
作者机构:[Yu, Yuanlong] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350002, Peoples R China.; [Gu, Jason] Dalhousie Univ, Dept Elect & Comp Engn, Halifax, NS, 更多
通讯作者地址:[Yu, YL]Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350002, Peoples R China.
来源:IET COMPUTER VISION
出版年:2014
卷:8
期:6
页码:510-522
DOI:10.1049/iet-cvi.2013.0149
摘要:This paper proposes a new unsupervised image segmentation method by using Bhattacharyya distance-based irregular pyramid, termed as 'BDIP' algorithm. The proposed BDIP algorithm obtains a suboptimal labelling solution under the condition that the number of segments is not manually given. It hierarchically builds each level of the irregular pyramid, with the result that the final segments emerge as they are represented by single nodes at certain levels. The BDIP algorithm employs Bhattacharyya distance to estimate the intra-level similarity at higher pyramidal levels so as to improve the accuracy and robustness to noise. Furthermore, an adaptive neighbour search method is proposed such that the BDIP algorithm can self-determine the number of segments. This method considers not only the graphic constraint, but also the similarity constraint in the sense that a candidate node is selected as a neighbour of the centre node if there is no boundary evidence between these two nodes. With the pyramidal accumulation, this evaluation is aggregated into the approximately global evidence, based on which the number of segments can be self-determined. Experimental results have shown that this proposed BDIP algorithm outperforms other benchmark segmentation algorithms in terms of segmentation accuracy, labelling cost and robustness to noise.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84915805105&doi=10.1049%2fiet-cvi.2013.0149&partnerID=40&md5=1ebcb0db6bfd7aa9dee6df5c3db79f39
TOP