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Dissimilarity Representation of Images for Relevance Feedback in Content-Based Image Retrieval

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Book cover Machine Learning and Data Mining in Pattern Recognition (MLDM 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2734))

Abstract

Relevance feedback mechanisms are adopted to refine image-based queries by asking users to mark the set of retrieved images as being relevant or not. In this paper, a relevance feedback technique based on the “dissimilarity representation” of images is proposed. Each image is represented by a vector whose components are the similarity values between the image itself and a “representation set” made up of the images retrieved so far. A relevance score is then assigned to each image according to its distances from the sets of relevant and non-relevant images. Three techniques to compute such relevance scores are described. Reported results on three image databases show that the proposed relevance feedback mechanism allows attaining large improvements in retrieval precision after each retrieval iteration. It also outperforms other techniques proposed in the literature.

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Author information

Authors and Affiliations

  1. Dept. of Electrical and Electronic Engineering, University of Cagliari, Piazza D’Armi 09123, Cagliari, Italy

    Giorgio Giacinto & Fabio Roli

Authors
  1. Giorgio Giacinto
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  2. Fabio Roli
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Arndtstr. 4, 04275, Leipzig, Germany

    Petra Perner

  2. Center for Automation Research, University of Maryland, College Park, Maryland, 20742-3275, USA

    Azriel Rosenfeld

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© 2003 Springer-Verlag Berlin Heidelberg

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Giacinto, G., Roli, F. (2003). Dissimilarity Representation of Images for Relevance Feedback in Content-Based Image Retrieval. In: Perner, P., Rosenfeld, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2003. Lecture Notes in Computer Science, vol 2734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45065-3_18

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  • DOI: https://doi.org/10.1007/3-540-45065-3_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40504-7

  • Online ISBN: 978-3-540-45065-8

  • eBook Packages: Springer Book Archive

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