Performance evaluation of local descriptors for maximally stable extremal regions

Visual feature descriptors are widely used in most computer vision applications. Over the past several decades, local feature descriptors that are robust to challenging environments have been proposed. Because their characteristics differ according to the imaging condition, it is necessary to compare their performance consistently. However, no pertinent research has attempted to establish a benchmark for performance evaluation, especially for affine region detectors, which are mainly used in object classification and recognition. This paper presents an intensive and informative performance evaluation of local descriptors for the state-of-the-art affine-invariant region detectors, i.e., maximally stable extremal region detectors. We evaluate patch-based and binary descriptors, including SIFT, SURF, BRIEF, FREAK, the shape descriptor, LIOP, DAISY, GSURF, RFDg, and CNN descriptors. The experimental results reveal the relative performance and characteristics of each descriptor.