Spatial locality-aware sparse coding and dictionary learning

Nonlinear encoding of SIFT features has recently shown good promise in image classification. This scheme is able to reduce the training complexity of the traditional bag-of-feature approaches while achieving better performance. As a result, it is suitable for large-scale image classification applications. However, existing nonlinear encoding methods do not explicitly consider the spatial relationship when encoding the local features, but merely leaving the spatial information used at a later stage, e.g. through the spatial pyramid matching, is largely inadequate. In this paper, we propose a joint sparse coding and dictionary learning scheme that take the spatial information into consideration in encoding. Our experiments on synthetic data and benchmark data demonstrate that the proposed scheme can learn a better dictionary and achieve higher classification accuracy.