Mendeley
TY  _  JOUR
ID  -  1396090813122819395
TI  -  A Novel Cascading Scheme to Improve Speed and Accuracy of a VMMR System
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Biglari M.
AU  -  Soleimani ali
AU  -  Hassanpour Hamid
AD  -  دانشگاه صنعتی شاهرود
AD  -  دانشگاه صنعتی شاهرود
AD  -  دانشگاه صنعتی شاهرود
Y1  -  2017
PY  -  2017
VL  -  3
IS  -  
SP  -  200
EP  -  210
KW  -  Object recognition
object classification
vehicle make and model recognition (VMMR)
cascade processing
cascading scheme
DO  -  
N2  -  In the last decade, many researches have been done on fine-grained recognition. 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. Furthermore, improving system accuracy leads to increasing in processing time. As we can see the state-of-the-art machine vision tool like convolutional neural networks lacks in real-time processing time. In this paper, a method has been presented briefly for VMMR firstly. Secondly, a cascading scheme for improving both speed and accuracy of this VMMR system has been proposed. In order to eliminate extra processing cost, the proposed cascading scheme applies classifiers to the input image in a sequential manner. Some effective criterions for an efficient ordering of classifiers are proposed and finally a fusion of them is used in the cascade algorithm. For evaluation purposes, a new dataset with more than 5000 vehicles of 28 different makes and models has been collected. The experimental results on this dataset and comprehensive CompCars dataset show outstanding performance of our approach. Our cascading scheme results up to 80% increase in the system processing speed.
UR  - rimag.ir/en/Article/28273
L1  - rimag.ir/en/Article/Download/28273
ER  -  
