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. © 2009 SPIE-IS&T.

Schettini, R., Cusano, C., Ciocca, G. (2009). Semantic classification, low level features and relevance feedback for content-based image retrieval. In Multimedia Content Access: Algorithms and Systems Conference III, IS&T/SPIE Symposium on Electronic Imaging (pp.72550D). SPIE [10.1117/12.810792].

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

SCHETTINI, RAIMONDO;CUSANO, CLAUDIO;CIOCCA, GIANLUIGI
2009

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. © 2009 SPIE-IS&T.
paper
Image Classification, Image Annotation, Relevance Feedback, Content Based Image Retrieval.
English
Multimedia Content Access: Algorithms and Systems Conference III, IS&T/SPIE Symposium on Electronic Imaging
Multimedia Content Access: Algorithms and Systems Conference III, IS&T/SPIE Symposium on Electronic Imaging
9780819475053
2009
7255
72550D
none
Schettini, R., Cusano, C., Ciocca, G. (2009). Semantic classification, low level features and relevance feedback for content-based image retrieval. In Multimedia Content Access: Algorithms and Systems Conference III, IS&T/SPIE Symposium on Electronic Imaging (pp.72550D). SPIE [10.1117/12.810792].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/12990
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