Title :
Experience-based relevance feedback for image retrieval
Author :
Ng, C.U. ; Martin, G.R.
Author_Institution :
Dept. of Comput. Sci., Univ. of Warwick, Coventry, UK
fDate :
Nov. 30 2005-Dec. 1 2005
Abstract :
This paper presents a novel search technique, ´Experience-based Relevance Feedback´ (XRF), formed from the fusion of keyword-based image retrieval (KBIR) and content-based image retrieval (CBIR). The technique significantly reduces the semantic-visual content gap apparent in most existing image retrieval systems. Information about previous searches is recorded and analysed in order to improve the current performance. Semantic knowledge is incorporated in the retrieval process, however, unlike KBIR the actual annotation is totally transparent to the user. As with many CBIR systems, XRF utilises relevance feedback (RF) for supervised learning and is able to retrieve visually similar images. It learns about the user´s preferences during the search process and utilises past experience to enhance current and future retrieval operations.
Keywords :
content-based retrieval; image retrieval; learning (artificial intelligence); query formulation; CBIR; KBIR; content-based image retrieval; experience-based relevance feedback; keyword-based image retrieval; search technique; semantic knowledge; semantic-visual content gap; supervised learning;
Conference_Titel :
Integration of Knowledge, Semantics and Digital Media Technology, 2005. EWIMT 2005. The 2nd European Workshop on the (Ref. No. 2005/11099)
Conference_Location :
London
Print_ISBN :
0-86341-595-4