Discriminative learned dictionaries for local image analysis

Sparse signal models have been the focus of much recent research, leading to (or improving upon) state-of-the-art results in signal, image, and video restoration. This article extends this line of research into a novel framework for local image discrimination tasks, proposing an energy formulation with both sparse reconstruction and class discrimination components, jointly optimized during dictionary learning. This approach improves over the state of the art in texture segmentation experiments using the Brodatz database, and it paves the way for a novel scene analysis and recognition framework based on simultaneously learning discriminative and reconstructive dictionaries. Preliminary results in this direction using examples from the Pascal VOC06 and Graz02 datasets are presented as well.

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro, Andrew Zisserman
• Keywords: Class Discrimination Components, Computer Vision, CVPR 2008, Image Discrimination Tasks, Recognition Framework, Sparse Reconstruction, Sparse Signal Models