Hybrid pooling fusion in the BoW pipeline

Abstract

In the context of object and scene recognition, state-of-the-art performances are obtained with Bag of Words (BoW) models of mid-level representations computed from dense sampled local descriptors (e.g. SIFT). Several methods to combine low-level features and to set mid-level parameters have been evaluated recently for image classification. In this paper, we further investigate the impact of the main parameters in the BoW pipeline. We show that an adequate combination of several low (sampling rate, multiscale) and mid level (codebook size, normalization) parameters is decisive to reach good performances. Based on this analysis, we propose a merging scheme exploiting the specificities of edge-based descriptors. Low and high-contrast regions are pooled separately and combined to provide a powerful representation of images. Sucessful experiments are provided on the Caltech-101 and Scene-15 datasets. © 2012 Springer-Verlag.