Independent query refinement and feature re-weighting using positive and negative examples for content-based image retrieval

Authors:
Cheng-Chin Chiang;Jyun-Yue Wu;Mau-Tsuen Yang;Wen-Kai Tai
Affiliations:
Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan 974;Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan 974;Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan 974;Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan 974
Venue:
Multimedia Tools and Applications
Year:
2009

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Abstract

Query refinement and feature re-weighting are the two core techniques underlying the relevance feedback of content-based image retrieval. Most existing relevance feedback mechanisms generally model the user's query target with a single query point and weight each extracted feature with a single importance factor. A designed estimation procedure then estimates the best query point and all importance factors by optimizing a formulated criterion which measures the goodness of the estimation. This formulated criterion simultaneously encapsulates all positive and negative examples supplied from the user's feedback. Under such formulation, the positive and negative examples may contribute contradictorily to the criterion and sometimes may introduce higher difficulty in attaining a good estimation. In this paper, we propose a different statistical formulation to estimate independently two pairs of query points and feature weights from the positive examples and negative examples, respectively. These two pairs then define the likelihood ratio, a criterion term used to rank the relevance of all database images. This approach simplifies the criterion formulation and also avoids the mutual impeditive influence between positive examples and negative examples. The experimental results demonstrate that the proposed approach outperforms some other related approaches.