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Proceedings Paper

Region-based hidden Markov models for image categorization and retrieval
Author(s): Fei Li; Qionghai Dai; Wenli Xu
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Paper Abstract

Hidden Markov models (HMMs) have been widely used in various fields, including image categorization and retrieval. Most of the existing methods train HMMs by low-level features of image blocks; however, the blockbased features can not reflect high-level semantic concepts well. This paper proposes a new method to train HMMs by region-based features, which can be obtained after image segmentation. Our work can be characterized by two key properties: (1) Region-based HMM is adopted to achieve better categorization performance, for the region-based features accord with the human perception better. (2) Multi-layer semantic representation (MSR) is introduced to couple with region-based HMM in a long-term relevance feedback framework for image retrieval. The experimental results demonstrate the effectiveness of our proposal in both aspects of categorization and retrieval.

Paper Details

Date Published: 29 January 2007
PDF: 8 pages
Proc. SPIE 6508, Visual Communications and Image Processing 2007, 65081V (29 January 2007); doi: 10.1117/12.702780
Show Author Affiliations
Fei Li, Tsinghua Univ. (China)
Qionghai Dai, Tsinghua Univ. (China)
Wenli Xu, Tsinghua Univ. (China)

Published in SPIE Proceedings Vol. 6508:
Visual Communications and Image Processing 2007
Chang Wen Chen; Dan Schonfeld; Jiebo Luo, Editor(s)

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