Vehicle Logo Recognition Using SIFT Representation and SVM

Abstract

We propose a vehicle logo recognition method that uses SIFT representation and SVM classification. At the training phase, for each training example, a region of interest (ROI) containing the vehicle logo is extracted based on the vehicle plate location, SIFT features are extracted from the ROI, and keywords as well as their counts are obtained by clustering the SIFT features. For all the training examples, their keywords as well as the corresponding counts are used as input and their categories are used as output for training an SVM classifier. At the recognition stage, by a similar procedure of the training stage, for each test example, SIFT features of the ROI are extracted, and keywords as well as their counts are generated by clustering. These keywords as well as their counts are used as input to the SVM classifier and the category of the vehicle logo is obtained. The method is dependent on processing of a ROI rather than on accurate location of the vehicle logo. It uses little prior knowledge, and is easy to use. The method provides a satisfactory recognition rate, and thus is a feasible method for fusion of multiple classifiers.