Multiresolution Laplacian sparse coding technique for image representation

Abstract

Sparse coding techniques have given good results in different domains especially in feature quantization and image representation. However, the major weakness of those techniques is their inability to represent the similarity between features. This limitation is due to the separate representation of features. Although the Laplacian sparse coding doesn’t focus on the spatial similarity in the image space, it preserves the locality of the features only in the data space. Due to this, the similarity between two local features belong to the similarity of their spatial neighborhood in the image. To overcome this flaw, we propose the integration of similarity based on Kullback-Leibler and wavelet decomposition in the domain of an image. This technique may surmount those limitations by taking into account each element of an image and its neighbors in similarity calculation. Classifications rates given by our approach show a clear improvement compared to those cited in this article.