Unsupervised image segmentation based on analysis of binary partition tree for salient object extraction

Authors:
Zhi Liu;Liquan Shen;Zhaoyang Zhang
Affiliations:
School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China and Key Lab of Advanced Display and System Application (Shanghai University), Ministry of Education ...;School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China and Key Lab of Advanced Display and System Application (Shanghai University), Ministry of Education ...;School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China and Key Lab of Advanced Display and System Application (Shanghai University), Ministry of Education ...
Venue:
Signal Processing
Year:
2011

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Abstract

This paper proposes an unsupervised image segmentation approach aimed at salient object extraction. Starting from an over-segmentation result of a color image, region merging is performed using a novel dissimilarity measure considering the impact of color difference, area factor and adjacency degree, and a binary partition tree (BPT) is generated to record the whole merging sequence. Then based on a systematic analysis of the evaluated BPT, an appropriate subset of nodes is selected from the BPT to represent a meaningful segmentation result with a small number of segmented regions. Experimental results demonstrate that the proposed approach can obtain a better segmentation performance from the perspective of salient object extraction.