Generalized Lasso based Approximation of Sparse Coding for Visual Recognition

Sparse coding, a method of explaining sensory data with as few dictionary bases as possible, has attracted much attention in computer vision. For visual object category recognition, `1 regularized sparse coding is combined with the spatial pyramid representation to obtain state-of-the-art performance. However, because of its iterative optimization, applying sparse coding onto every local feature descriptor extracted from an image database can become a major bottleneck. To overcome this computational challenge, this paper presents “Generalized Lasso based Approximation of Sparse coding” (GLAS). By representing the distribution of sparse coefficients with slice transform, we fit a piece-wise linear mapping function with the generalized lasso. We also propose an efficient post-refinement procedure to perform mutual inhibition between bases which is essential for an overcomplete setting. The experiments show that GLAS obtains a comparable performance to `1 regularized sparse coding, yet achieves a significant speed up demonstrating its effectiveness for large-scale visual recognition problems.