Component-based feature extraction and representation schemes for vehicle make and model recognition

Abstract This paper presents a novel feature representation and recognition scheme for vehicle make and model recognition (VMMR) from the frontal image of the vehicle. In general, some domain knowledge is introduced and further exploited to accomplish this task. The modular components in the frontal appearance of vehicles present distinct visual characteristics, and the varying discrimination ability of them made the main discriminant region changes when vehicles compared at inter- or intra-brand level. Inspired by the peculiar vehicle properties, this paper focuses on the localized visual characteristics in the discriminant subregions, and make the representation of the frontal vehicle in a multi-scale spatial manner. This representation scheme encodes local spatial information and component-specific characteristics to the feature descriptors, which enhances the discrimination ability of the feature representation and mitigates the multiplicity problem of VMMR to some extent. Extensive experiments on a large-scale vehicle image dataset have shown the efficiency of the methods in this work.