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Distribution-Based Image Similarity

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Book cover State-of-the-Art in Content-Based Image and Video Retrieval

Part of the book series: Computational Imaging and Vision ((CIVI,volume 22))

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Abstract

Image similarity measures are at the core of every image retrieval system. In this contribution, we provide a systematic overview of distribution based measures for image similarity. We then empirically compare nine families of color and texture similarity measures summarizing over 1,000 CPU hours of computational experiments. Quantitative performance evaluations are given for classification and image retrieval.

Based on the empirical findings a novel image retrieval framework is developed relying on the following fundamental design decisions: First, database items are described by generative probabilistic models. Second, similarity between a query and a database image is measured in terms of how well the corresponding generative model describes or explains the new query. Besides its statistical foundation the proposed procedure has the following key advantages: (i) The probabilistic models can be estimated independently from each other. Thus no joint histogram binning for the complete database as in most commonly employed methods is necessary. (ii) It is possible to model different cues for different images. (iii) The approach can naturally be extended to more refined models.

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Author information

Authors and Affiliations

  1. Dept. Computer Science, University of California, Berkeley, USA

    Jan Puzicha

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  1. Jan Puzicha
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Editor information

Editors and Affiliations

  1. Utrecht University, The Netherlands

    Remco C. Veltkamp

  2. University of Freiburg, Germany

    Hans Burkhardt

  3. University of Munich, Germany

    Hans-Peter Kriegel

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© 2001 Springer Science+Business Media Dordrecht

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Puzicha, J. (2001). Distribution-Based Image Similarity. In: Veltkamp, R.C., Burkhardt, H., Kriegel, HP. (eds) State-of-the-Art in Content-Based Image and Video Retrieval. Computational Imaging and Vision, vol 22. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9664-0_7

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  • DOI: https://doi.org/10.1007/978-94-015-9664-0_7

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-5863-8

  • Online ISBN: 978-94-015-9664-0

  • eBook Packages: Springer Book Archive

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