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Proceedings Paper

Semantic classification, low level features, and relevance feedback for content-based image retrieval
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Paper Abstract

Although traditional content-based retrieval systems have been successfully employed in many multimedia applications, the need for explicit association of higher concepts to images has been a pressing demand from users. Many research works have been conducted focusing on the reduction of the semantic gap between visual features and the semantics of the image content. In this paper we present a mechanism that combines broad high level concepts and low level visual features within the framework of the QuickLook content-based image retrieval system. This system also implements a relevance feedback algorithm to learn users' intended query from positive and negative image examples. With the relevance feedback mechanism, the retrieval process can be efficiently guided toward the semantic or pictorial contents of the images by providing the system with the suitable examples. The qualitative experiments performed on a database of more than 46,000 photos downloaded from the Web show that the combination of semantic and low level features coupled with a relevance feedback algorithm, effectively improve the accuracy of the image retrieval sessions.

Paper Details

Date Published: 19 January 2009
PDF: 10 pages
Proc. SPIE 7255, Multimedia Content Access: Algorithms and Systems III, 72550D (19 January 2009); doi: 10.1117/12.810792
Show Author Affiliations
G. Ciocca, Univ. degli Studi di Milano-Bicocca (Italy)
C. Cusano, Univ. degli Studi di Milano-Bicocca (Italy)
R. Schettini, Univ. degli Studi di Milano-Bicocca (Italy)


Published in SPIE Proceedings Vol. 7255:
Multimedia Content Access: Algorithms and Systems III
Raimondo Schettini; Ramesh C. Jain; Simone Santini, Editor(s)

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