BRIGHT: A scalable and compact binary descriptor for low-latency and high accuracy object identification

This paper proposes a new scalable and compact binary local descriptor, named the BRIGHT (Binary ResIzable Gradient HisTogram) descriptor, for low-latency and high accuracy identification of real-world objects in images. The BRIGHT descriptor is extracted by first creating a hierarchical HOG (Histogram of Oriented Gradients) of a local patch centered around keypoints detected from an image. The elements of the histogram are then binarized, and the subset of bits is progressively selected forming a progressively scalable descriptor with a size ranging from only 32 bits to 150 bits. Experiment using images with objects taken under various camera viewpoints, lighting conditions, and occlusions, shows that the BRIGHT descriptor can robustly match objects with an identification accuracy comparable with that of SIFT descriptor, but at a descriptor size smaller than 1/10 of SIFT. With the reduced descriptor size, transmission of descriptors from a mobile device to a database server can be dramatically speeded up, enabling low-latency response in mobile search services.