Learning discriminative spatial representation for image classification

Spatial Pyramid Representation (SPR) [7] introduces spatial layout information to the orderless bag-of-features (BoF) representation. SPR has become the standard and has been shown to perform competitively against more complex methods for incorporating spatial layout. In SPR the image is divided into regular grids. However, the grids are taken as uniform spatial partitions without any theoretical motivation. In this paper, we address this issue and propose to learn the spatial partitioning with the BoF representation. We define a space of grids where each grid is obtained by a series of recursive axis aligned splits of cells. We cast the classification problem in a maximum margin formulation with the optimization being over the weight vector and the spatial grid. In addition to experiments on two challenging public datasets (Scene-15 and Pascal VOC 2007) showing that the learnt grids consistently perform better than the SPR while being much smaller in vector length, we also introduce a new dataset of human attributes and show that the current method is well suited to the recognition of spatially localized human attributes.