Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation
Julien Mairal, Marius Leordeanu, Francis Bach, Martial Hebert and Jean Ponce
In: ECCV 2008, Marseille, France(2008).
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.
|EPrint Type:||Conference or Workshop Item (Oral)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Francis Bach|
|Deposited On:||13 March 2009|