Region-based hidden Markov models for image categorization and retrieval

Fei Li; Qionghai Dai; Wenli Xu

doi:10.1117/12.702780

29 January 2007 Region-based hidden Markov models for image categorization and retrieval

Fei Li, Qionghai Dai, Wenli Xu

Proceedings Volume 6508, Visual Communications and Image Processing 2007; 65081V (2007) https://doi.org/10.1117/12.702780
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

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.

Citation Download Citation

Fei Li, Qionghai Dai, and Wenli Xu "Region-based hidden Markov models for image categorization and retrieval", Proc. SPIE 6508, Visual Communications and Image Processing 2007, 65081V (29 January 2007); https://doi.org/10.1117/12.702780

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