Semantic Classification, Low Level Features and Relevance Feedback for Content-Based Image Retrieval

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.