Adjacent coding for image classification

The locality and sparsity constrained encoding methods have shown the good image classification performance in recent papers. Among these methods, the common strategy is encoding one descriptor into one code by a learned codebook and then applying SPM and Pooling strategy to get the final image representation. However, the ignorance of local spatial context has been a barrier to improve their discriminative power. To address this problem, we propose the so called Adjacent Coding (AC), which employs the adjacency of one descriptor to express the local spatial context. Different from traditional coding methods, Adjacent Coding encodes one descriptor and its adjacent neighbors together. In this paper, we further show that AC also keeps the properties of locality and sparsity. Finally, our experiments on the standard benchmarks (Scene 15 and Caltech 101) show our method can outperform the state-of-the-art feature coding methods.