A Biased Sampling Strategy for Object Categorization

Abstract

In this paper, we present a biased sampling strategy for object class modeling, which can effectively circumvent the scene matching problem commonly encountered in statistical image-based object categorization. The method optimally combines the bottom-up, biologically inspired saliency information with loose, top-down class prior information to form a probabilistic distribution for feature sampling. When sampling over different positions and scales of patches, the weak spatial coherency is preserved by a segment-based analysis. We evaluate the proposed sampling strategy within the bag-of-features (BoF) object categorization framework on three public data sets. Our technique outperforms other state-of-the-art sampling technologies, and leads to a better performance in object categorization on VOC2008 dataset.