A novel dictionary learning algorithm for image representation

Sparse coding has proved its efficiency in the image classification task. However, its major drawback is the discarding of the spatial context information that can be extracted from the image. Therefore, we propose in this work a novel sparse coding method called Laplacian sparse coding based on the integration of topological information in the encoding process. This is achieved by embedding the similarities between local region visual phrases into the objective function of the classical Laplacian sparse coding. Experimental results made on several datasets prove the efficiency of the proposed method.