Image Segmentation Based on Contextual Label Tree for Retrieval

Abstract:

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Image segmentation is an important constituent portion in image processing and retrieval. Based on the traditional Wavelet-domain Hidden Markov Tree (HMT) Multi-scale Segmentation method, this paper presents a Contextual Label Tree (CLT) method according to the dependency information between image blocks belong to different scales, including the relation from the father node, the neighbor nodes and the neighbor nodes of the father. This method calculates the maximal similarity using context vectors that exit on every tree node and realizes image segmentation from coarse-scale to fine-scale. Experiments show that this method is satisfied with its segmentation performance.

Info:

Periodical:

Advanced Materials Research (Volumes 121-122)

Edited by:

Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo

Pages:

563-568

DOI:

10.4028/www.scientific.net/AMR.121-122.563

Citation:

G. Q. Yuan and H. S. Liu, "Image Segmentation Based on Contextual Label Tree for Retrieval", Advanced Materials Research, Vols. 121-122, pp. 563-568, 2010

Online since:

June 2010

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Price:

$35.00

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