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Co-Sparse Textural Similarity for Interactive Segmentation

  • Claudia Nieuwenhuis
  • Simon Hawe
  • Martin Kleinsteuber
  • Daniel Cremers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8694)

Abstract

We propose an algorithm for segmenting natural images based on texture and color information, which leverages the co-sparse analysis model for image segmentation. As a key ingredient of this method, we introduce a novel textural similarity measure, which builds upon the co-sparse representation of image patches. We propose a statistical MAP inference approach to merge textural similarity with information about color and location. Combined with recently developed convex multilabel optimization methods this leads to an efficient algorithm for interactive segmentation, which is easily parallelized on graphics hardware. The provided approach outperforms state-of-the-art interactive segmentation methods on the Graz Benchmark.

Keywords

Image Segmentation Natural Image Image Patch Textural Similarity Graphic Hardware 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Claudia Nieuwenhuis
    • 1
  • Simon Hawe
    • 2
  • Martin Kleinsteuber
    • 2
  • Daniel Cremers
    • 2
  1. 1.UC BerkeleyUSA
  2. 2.Technische Universität MünchenGermany

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