A two-level relevance feedback mechanism for image retrieval

Authors:
Pei-Cheng Cheng;Been-Chian Chien;Hao-Ren Ke;Wei-Pang Yang
Affiliations:
Department of Computer Science, National Chiao Tung University, 1001 Ta Hsueh Rd., Hsinchu 30050, Taiwan, ROC and Department of Information Management, Ching Yun University, 229, Chien-Hsin Road, ...;Department of Computer Science and Information Engineering, National University of Tainan, 33, Sec. 2, Su Line St., Tainan 70005, Taiwan, ROC;Institute of Information Management, National Chiao Tung University, 1001 Ta Hsueh Rd., Hsinchu 30050, Taiwan, ROC;Department of Information Management, National Dong Hwa University, 1, Sec. 2, Da Hsueh Rd., Shou-Feng, Hualien 97401, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

Content-based image retrieval (CBIR) is a group of techniques that analyzes the visual features (such as color, shape, texture) of an example image or image subregion to find similar images in an image database. Relevance feedback is often used in a CBIR system to help users express their preference and improve query results. Traditional relevance feedback relies on positive and negative examples to reformulate the query. Furthermore, if the system employs several visual features for a query, the weight of each feature is adjusted manually by the user or system predetermined and fixed by the system. In this paper we propose a new relevance feedback model suitable for medical image retrieval. The proposed method enables the user to rank the results in relevance order. According to the ranking, the system can automatically determine the importance ranking of features, and use this ranking to automatically adjust the weight of each feature. The experimental results show that the new relevance feedback mechanism outperforms previous relevance feedback models.