RESEARCH ON MULTI-LEVEL LOG-BASED RELEVANCE FEEDBACK SCHEME FOR IMAGE RETRIEVAL
Weifeng Sun, Jing Luo, Kaixian Hu, Chuang Lin
Abstract
Weifeng Sun, Jing Luo, Kaixian Hu, Chuang Lin
Abstract
Content-based Image Retrieval (CBIR) using relevance feedback technique is applied to improve the results of traditional techniques in image retrieva l. Since the results returned by system cannot full y satisfy users and the iteration process of feedback can be very time-consuming and tedious, log-based relevance feedback is introduce to the system. In previous wo rk, we have already introduced multi-level log-base d relevance feedback scheme for image retrieval to ac celerate the iteration process and to increase the hit rate. In this paper, we improve the novel algorithm and apply it in a demo image retrieval system whic h presents refined results based on multi-level log-b ased relevance feedback for Content-based Image Retrieval.
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Content-based Image Retrieval (CBIR) using relevance feedback technique is applied to improve the results of traditional techniques in image retrieva l. Since the results returned by system cannot full y satisfy users and the iteration process of feedback can be very time-consuming and tedious, log-based relevance feedback is introduce to the system. In previous wo rk, we have already introduced multi-level log-base d relevance feedback scheme for image retrieval to ac celerate the iteration process and to increase the hit rate. In this paper, we improve the novel algorithm and apply it in a demo image retrieval system whic h presents refined results based on multi-level log-b ased relevance feedback for Content-based Image Retrieval.
Key concepts: Relevance feedback, Image retrieval, Relevance (law), Computer science, Scheme (mathematics), Information retrieval, Process (computing), Image (mathematics)