Part-based recognition of vehicle make and model

Fine-grained recognition is a challenge that the computer vision community faces nowadays. The main category of the object is known in this problem and the goal is to determine the subcategory or fine-grained category. Vehicle make and model recognition (VMMR) is a hard fine-grained classification problem, due to the large number of classes, substantial inner-class and small inter-class distance. In this study, a novel approach has been proposed for VMMR based on latent SVM formulation. This approach automatically finds a set of discriminative parts in each class of vehicles by employing a novel greedy parts localisation algorithm, while learning a model per class using both features extracted from these parts and the spatial relationship between them. An effective and practical multi-class data mining method is proposed to filter out hard negative samples in the training procedure. Employing these trained individual models together, the authors’ system can classify vehicles make and model with a high accuracy. For evaluation purposes, a new dataset including more than 5000 vehicles of 28 different makes and models has been collected and fully annotated. The experimental results on this dataset and the CompCars dataset indicate the outstanding performance of the authors’ approach.