Extending Laplacian sparse coding by the incorporation of the image spatial context

Diverse studies have shown the efficiency of sparse coding in feature quantization. However, its major drawback is that it neglects the relationships among features. To reach the spatial context, we proposed in this paper, a novel sparse coding method called Extended Laplacian Sparse Coding. Two successive stages are required in this method. In the first stage, the sparse visual phrases based on Laplacian sparse coding are generated from the local regions in order to represent the geometric information in the image space. The second stage aims to incorporate the spatial relationships among local features in the image space into the objective function of the Laplacian sparse coding. It takes into account the similarity among local regions in the Laplacian sparse coding process. The matching between the local regions is based on the Hungarian method as well as the histogram intersection measure between sparse visual phrases already assigned to the local regions in the first stage. Furthermore, we suggested to improve the pooling step that succeeds the encoding step by introducing the discretized max pooling method that estimates the distribution of the responses of each local feature to the dictionary of basis vectors. Our experimental results prove that our method outperforms the existing background results.