Discriminative compact pyramids for object and scene recognition

Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets.

► Spatial pyramids (SP) major drawback is their high dimensional representation.
► We present a novel framework for obtaining compact SP up to an order of magnitude.
► Our method is based on the divisive information theoretic feature clustering (DITC).
► DITC outperforms the agglomerative information bottleneck clustering (AIB).
► We present an optimal strategy to combine multiple features within SP.