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Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation

  • Julien Mairal
  • Marius Leordeanu
  • Francis Bach
  • Martial Hebert
  • Jean Ponce
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5304)

Abstract

Sparse signal models learned from data are widely used in audio, image, and video restoration. They have recently been generalized to discriminative image understanding tasks such as texture segmentation and feature selection. This paper extends this line of research by proposing a multiscale method to minimize least-squares reconstruction errors and discriminative cost functions under ℓ0 or ℓ1 regularization constraints. It is applied to edge detection, category-based edge selection and image classification tasks. Experiments on the Berkeley edge detection benchmark and the PASCAL VOC’05 and VOC’07 datasets demonstrate the computational efficiency of our algorithm and its ability to learn local image descriptions that effectively support demanding computer vision tasks.

Keywords

Edge Detection Sparse Representation Reconstruction Error Sparse Code Orthogonal Match Pursuit 
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 2008

Authors and Affiliations

  • Julien Mairal
    • 1
    • 4
  • Marius Leordeanu
    • 2
  • Francis Bach
    • 1
    • 4
  • Martial Hebert
    • 2
  • Jean Ponce
    • 3
    • 4
  1. 1.INRIA, Paris-RocquencourtFrance
  2. 2.Carnegie Mellon UniversityPittsburgh
  3. 3.Ecole Normale SupérieureParis
  4. 4.WILLOW project-team, ENS/INRIA/CNRS UMR 8548France

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