Share Email Print
cover

Proceedings Paper

Region-based hidden Markov models for image categorization and retrieval
Author(s): Fei Li; Qionghai Dai; Wenli Xu
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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)

© SPIE. Terms of Use
Back to Top