Linear Spatial Pyramid Matching Using Non-convex and non-negative Sparse Coding for Image Classification
This is an incremental improvement for image classification tasks.
The paper tackled image classification by proposing an improved sparse coding model with simultaneous non-convex and non-negative constraints, showing superiority over the original ScSPM on several databases.
Recently sparse coding have been highly successful in image classification mainly due to its capability of incorporating the sparsity of image representation. In this paper, we propose an improved sparse coding model based on linear spatial pyramid matching(SPM) and Scale Invariant Feature Transform (SIFT ) descriptors. The novelty is the simultaneous non-convex and non-negative characters added to the sparse coding model. Our numerical experiments show that the improved approach using non-convex and non-negative sparse coding is superior than the original ScSPM[1] on several typical databases.