PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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).

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.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:4520
Deposited By:Francis Bach
Deposited On:13 March 2009