Relevance feedback has recently emerged as a solution to the problem of providing an effective response to a similarity query in an images retrieval system based on low-level information such as color, texture and shape features. This paper describes an approach for learning an optimal similarity metric based on the analysis of relevant and non-relevant information given by the user during the feedback process. A positive and a negative space are determined as an approximation of the examples given by the user. The relevant region is represented by a KL subspace of positive examples and is iteratively updated at each feedback iteration. The non- relevant region is modeled by a MKL space, which better characterizes the variety of negative examples, which very likely could belong to more than one class. The search process is, then, formulated as a classification problem, based on the calculation of the minimal distance to the relevant or non-relevant region.

A new approach for relevance feedback through positive and negative samples

FRANCO, ANNALISA;LUMINI, ALESSANDRA;MAIO, DARIO
2004

Abstract

Relevance feedback has recently emerged as a solution to the problem of providing an effective response to a similarity query in an images retrieval system based on low-level information such as color, texture and shape features. This paper describes an approach for learning an optimal similarity metric based on the analysis of relevant and non-relevant information given by the user during the feedback process. A positive and a negative space are determined as an approximation of the examples given by the user. The relevant region is represented by a KL subspace of positive examples and is iteratively updated at each feedback iteration. The non- relevant region is modeled by a MKL space, which better characterizes the variety of negative examples, which very likely could belong to more than one class. The search process is, then, formulated as a classification problem, based on the calculation of the minimal distance to the relevant or non-relevant region.
Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2004)
905
908
A. Franco; A. Lumini; D. Maio
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/6556
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