Unsupervised image segmentation based on analysis of binary partition tree for salient object extraction. (English) Zbl 1203.94012
Summary: 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.
MSC:
| 94A08 | Image processing (compression, reconstruction, etc.) in information and communication theory |
| 05C90 | Applications of graph theory |
Software:
BSDSReferences:
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