Bag of Words (BOW) models are nowadays one of the most effective methods for visual categorization. They use visual dictionaries to aggregate the set of local descriptors extracted from a given image. Despite their high discriminative ability, one of the major drawbacks of BOW still remains the computational cost of the visual dictionary, built by clustering in the high dimensional feature space.
In this paper we introduce a fast, effective method for local image descriptors aggregation that is based on marginal approximations, i.e. the approximation of each descriptor
component distribution. We quantize each dimension of the feature space, obtaining a visual alphabet that we use to map the image descriptors in a fixed-length visual signature. Experimental results show that our new method outperforms the traditional BOW model in both accuracy and efficiency for the scene recognition task. Moreover, we discover that
the marginal-based aggregation provides complementary information with respect to BOW, by combining the two models in a video retrieval system based on TRECVID 2010 .