Combined Automatic Weighting and Relevance Feedback Method in Content-Based Image Retrieval
Relevance Feedback (RF) is a powerful technique in Content-Based Image Retrieval (CBIR) system and has become a very active research topic in the past few years. At the early stage of CBIR, research primarily focused on exploring various feature representation and ignored the subjectivity of human perception. There exists a gap between highlevel concepts and low-level features. As an effective solution, the RF technique has been used on many CBIR systems to improve the retrieval precision. In this paper, a combined automatic weighting and relevance feedback method is proposed to improve the retrieval performance of CBIR. An approach using genetic algorithm for computing the initial weight of feature vector was introduced. By moving the query vector and updating the weighting factors simultaneously, the convergence speed of the relevance feedback retrieval is accelerated. Experimental results show that this method achieves high accuracy and effectiveness in CBIR.
content-based image retrieval relevance
Yubing Dong
Electronic and Information Engineering Department Changchun University Changchun,P.R.China
国际会议
长春
英文
179-182
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）