Data Driven Convolutional Sparse Coding for Visual Recognition

Abstract

Convolutional sparse coding (CSC) has become an important method in image processing and computer vision. In this paper we focus on visual recognition problems and apply CSC as a feature learning method. We propose a task-specific approach to treat the dictionary of CSC as parameters for a larger learning framework. These parameters are differentiable under mild conditions, and could be updated end-to-end using back-propagation when the errors from the task objectives are provided. We perform several experiments to show that such method provides a more discriminate representation compared with previous CSC methods, and this data driven approach is effective for visual recognition problems.