Hierarchical sparse coding based on spatial pooling and multi-feature fusion
We propose a novel hierarchical sparse coding algorithm with spatial pooling and multi-feature fusion, to construct the low-level visual primitives, e.g., local image patches or regions, into high-level visual phrases, e.g., image patterns. In the first layer we learn the sparse codes for the visual primitives and then pass them into the second layer by spatial pooling and multi-feature fusion. In the second layer we further learn the sparse codes for the visual phrases. In order to obtain the high-quality representations for visual phrases, our proposed algorithm iteratively optimizes over the two-layer sparse codes, as well as the two-layer codebooks. Since we have explored both the spatial and multi-feature contextual information, more representative sparse codes of the visual phrases can be obtained. The experiments on image pattern discovery, image scene clustering and image classification justify the advantages of the proposed algorithm.