標題: A two-level relevance feedback mechanism for image retrieval
作者: Cheng, Pei-Cheng
Chien, Been-Chian
Ke, Hao-Ren
Yang, Wei-Pang
資訊工程學系
Department of Computer Science
關鍵字: content-based image retrieval;relevance feedback;image database
公開日期: 1-Apr-2008
摘要: 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. (c) 2007 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.eswa.2007.02.030
http://hdl.handle.net/11536/9533
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2007.02.030
期刊: EXPERT SYSTEMS WITH APPLICATIONS
Volume: 34
Issue: 3
起始頁: 2193
結束頁: 2200
Appears in Collections:Articles


Files in This Item:

  1. 000253183700061.pdf