Locality-constrained and spatially regularized coding for scene categorization

Improving coding and spatial pooling for bag-of-words based feature design have gained a lot of attention in recent works addressing object recognition and scene classification. Regarding the coding step in particular, properties such as sparsity, locality and saliency have been investigated. The main contribution of this work consists in taking into acount the local spatial context of an image into the usual coding strategies proposed in the state-of-the-art. For this purpose, given an imgae, dense local features are extracted and structured in a lattice. The latter is endowed with a neighborhood system and pairwise interactions. We propose a new objective function to encode local features, which preserves locality constraints both in the feature space and the spatial domain of the image. In addition, an appropriate efficient optimization algorithm is provided, inspired from the graph-cut framework. In conjunction with the maximum-pooling operation and the spatial pyramid matching, that reflects a global spatial layout, the proposed method improves the performances of several state-of-the-art coding schemes for scene classification on three publicly available benchmarks (UIUC 8-sport, Scene-15 and Caltech-101).