Evaluation of low-complexity visual feature detectors and descriptors
Antonio Canclini, Matteo Cesana, Alessandro E. C. Redondi, Marco Tagliasacchi, João Ascenso, Rodrigo Cilla · 2013
Several visual feature extraction algorithms have recently appeared in the literature, with the goal of reducing the computational complexity of state-of-the-art solutions (e.g., SIFT and SURF). Therefore, it is necessary to evaluate the performance of these emerging visual descriptors in terms of processing time, repeatability and matching accuracy, and whether they can obtain competitive performance in applications such as image retrieval. This paper aims to provide an up-to-date detailed, clear, and complete evaluation of local feature detector and descriptors, focusing on the methods that were designed with complexity constraints, providing a much needed reference for researchers in this field. Our results demonstrate that recent feature extraction algorithms, e.g., BRISK and ORB, have competitive performance requiring much lower complexity and can be efficiently used in low-power devices.