Local Histogram Based Descriptors For Recognition

This paper proposes a set of new image descriptors based on local histograms of basic operators. These descriptors are intended to serve in a first-level stage of an hierarcical representation of image structures. For reasons of efficiency and scalability, we argue that descriptors suitable for this purpose should be able to capture and separate invariant and variant properties. Unsupervised clustering of the image descriptors from training data gives a visual vocabulary, which allow for compact representations. We demonstrate the representational power of the proposed descriptors and vocabularies on image categorization tasks using well-known datasets. We use image representations via statistics in form of global histograms of the underlying visual words, and compare our results to earlier reported work.