Semantic Kernels Binarized - A Feature Descriptor for Fast and Robust Matching

Abstract

This paper presents a new approach for feature description used in image processing and robust image recognition algorithms such as 3D camera tracking, view reconstruction or 3D scene analysis. State of the art feature detectors distinguish interest point detection and description. The former is commonly performed in scale space, while the latter is used to describe a normalized support region using histograms of gradients or similar derivatives of the grayscale image patch. This approach has proven to be very successful. However, the descriptors are usually of high dimensionality in order to achieve a high descriptiveness. Against this background, we propose a binarized descriptor which has a low memory usage and good matching performance. The descriptor is composed of binarized responses resulting from a set of folding operations applied to the normalized support region. We demonstrate the real-time capabilities of the feature descriptor in a stereo matching environment.