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International Symposium on Visual Computing

ISVC 2013: Advances in Visual Computing pp 496–506Cite as

Collaborative Sparse Representation in Dissimilarity Space for Classification of Visual Information

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8033))

Abstract

In this work we perform a thorough evaluation of the most popular CR-based classification scheme, the SRC, on the task of classification in dissimilarity space. We examine the performance utilizing a large set of public domain dissimilarity datasets mainly derived from classification problems relevant to visual information. We show that CR-based methods can exhibit remarkable performance in challenging situations characterized by extreme non-metric and non-Euclidean behavior, as well as limited number of available training samples per class. Furthermore, we investigate the structural qualities of a dataset necessitating the use of such classifiers. We demonstrate that CR-based methods have a clear advantage on dissimilarity data stemming from extended objects, manifold structures or a combination of these qualities. We also show that the induced sparsity during CR, is of great significance to the classification performance, especially in cases with small representative sets in the training data and large number of classes.

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Author information

Authors and Affiliations

  1. Electronics Laboratory, Department of Physics, University of Patras, Patras, Greece

    Ilias Theodorakopoulos, George Economou & Spiros Fotopoulos

Authors
  1. Ilias Theodorakopoulos
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  2. George Economou
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  3. Spiros Fotopoulos
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Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Arizona State University, Tempe, AZ, USA

    Baoxin Li

  6. Mitsubishi Electric Research Laboratories, Cambridge, MA, USA

    Fatih Porikli

  7. University of California, Riverside, CA, USA

    Victor Zordan

  8. AT&T Research Labs, Florham Park, NJ, USA

    James Klosowski

  9. ZIRST, Saint-Ismier Cedex, France

    Sabine Coquillart

  10. Qualcomm Research, San Diego, CA, USA

    Xun Luo

  11. Oxford e-Research Centre, University of Oxford, Oxford, UK

    Min Chen

  12. IBM, Hawthorne, NY, USA

    David Gotz

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© 2013 Springer-Verlag Berlin Heidelberg

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Theodorakopoulos, I., Economou, G., Fotopoulos, S. (2013). Collaborative Sparse Representation in Dissimilarity Space for Classification of Visual Information. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41914-0_49

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  • DOI: https://doi.org/10.1007/978-3-642-41914-0_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41913-3

  • Online ISBN: 978-3-642-41914-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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