Vehicle logo recognition using histograms of oriented gradient descriptor and sparsity score 
	Kittikhun Meethongjan, Thongchai Surinwarangkoon, Vinh Truong Hoang 
	
			
		Abstract 
		
		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 requirea 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 featureĀ  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.
		
		 
	
			
		Keywords 
		
		feature selection; HOG descriptor; image classification; sparsity score; vehicle logo recognition;
		
		 
	
				
			
	
	
							
		
		DOI: 
http://doi.org/10.12928/telkomnika.v18i6.16133 	
Refbacks 
				There are currently no refbacks. 
	 
				
		This work is licensed under a 
Creative Commons Attribution-ShareAlike 4.0 International License .
	
TELKOMNIKA Telecommunication, Computing, Electronics and Control 1693-6930 , e-ISSN: 2302-9293 Universitas Ahmad Dahlan , 4th Campus+62  274 564604
<div class="statcounter"><a title="Web Analytics" href="http://statcounter.com/" target="_blank"><img class="statcounter" src="//c.statcounter.com/10241713/0/0b6069be/0/" alt="Web Analytics"></a></div>  View TELKOMNIKA Stats