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

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    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

    Color Constancy Algorithm Selection Using CART

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    In this work, we investigate how illuminant estimation techniques can be improved taking into account intrinsic, low level properties of the images. We show how these properties can be used to drive, given a set of illuminant estimation algorithms, the selection of the best algorithm for a given image. The selection is made by a decision forest composed by several trees that vote for one of the illuminant estimation algorithm. The most voted algorithm is then applied to the input image. Experimental results on the widely used Ciurea and Funt dataset demonstrate the accuracy of our approach in comparison to other algorithms in the state of the art. © 2009 Springer Berlin Heidelberg
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