Bhattacharyya distance‐based irregular pyramid method for image segmentation. Issue 6 (1st December 2014)

• Record Type: Journal Article
• Authors: Yu, Yuanlong; Gu, Jason; Wang, Junzheng
• Is Part Of: IET computer vision. Volume 8:Issue 6(2014)
• Page Start: 510
• Page End: 522
• Publication Date: 2014-12-01
• Subjects: image representation -- image segmentation -- search problems; Bhattacharyya distance-based irregular pyramid method -- unsupervised image segmentation method -- BDIP algorithm -- suboptimal labelling solution -- image representation -- adaptive neighbour search method -- global evidence approximation -- benchmark segmentation algorithm -- intralevel similarity estimation; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2013.0149
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : 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.