Sparse Multiscale Patches (SMP) for Image Categorization

  • Paolo Piro
  • Sandrine Anthoine
  • Eric Debreuve
  • Michel Barlaud
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5371)


In this paper we address the task of image categorization using a new similarity measure on the space of Sparse Multiscale Patches (SMP). SMP s are based on a multiscale transform of the image and provide a global representation of its content. At each scale, the probability density function (pdf) of the SMP s is used as a description of the relevant information. The closeness between two images is defined as a combination of Kullback-Leibler divergences between the pdfs of their SMP s.

In the context of image categorization, we represent semantic categories by prototype images, which are defined as the centroids of the training clusters. Therefore any unlabeled image is classified by giving it the same label as the nearest prototype. Results obtained on ten categories from the Corel collection show the categorization accuracy of the SMP method.


Graphic Processing Unit Training Image Image Categorization Query Image Average Recognition Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Paolo Piro
    • 1
  • Sandrine Anthoine
    • 1
  • Eric Debreuve
    • 1
  • Michel Barlaud
    • 1
  1. 1.University of Nice-Sophia Antipolis / CNRSFrance

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