Vehicle Logo Recognition using HOG descriptor and sparsity score

Kittikhun Meethongjan, Thongchai Surinwarangkoon, Vinh Truong Hoang


Most of vehicle have the similar structures and designs. It is extremely complicated and difficult to identify and classify vehicle brands based on their structure and shape. As we require a quick and reliable response, so vehicle logos are an alternative method of determining the type of a vehicle. In this paper, we propose a method for vehicle logo recognition based on feature selection method in a hybrid way. Vehicle logo images are first characterized by Histograms of Oriented Gradient descriptors and the final features vector are then applied feature selection method to reduce the irrelevant information. Moreover, we release a new benchmark dataset for vehicle logo recognition and retrieval task namely, VLR-40. The experimental results are evaluated on this database which show the efficiency of the proposed approach.


vehicle logo recognition; image classification; feature selection; sparsity score; HOG descriptor;


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TELKOMNIKA Telecommunication, Computing, Electronics and Control
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