TY - GEN
T1 - A convenient feature vector construction for vehicle color recognition
AU - Dule, Erida
AU - Gökmen, Muhittin
AU - Beratoǧlu, M. Sabur
PY - 2010
Y1 - 2010
N2 - Given outdoor vehicle images, we try to find an acceptable method chain that maximizes the vehicle color recognition success. Our aim is to determine the color of the vehicle located in a colored image and to make a decision among the chosen seven color classes. At this study, performances of different feature sets obtained by various color spaces and different classification methods are taken to account in order to improve the outdoor vehicle color recognition. Also, different Region of Interest (ROI) and feature vector construction methods are developed for gain better performance. We examined two ROI (smooth hood peace and semi front vehicle), three classification methods (K-Nearest Neighbors, Artificial Neural Networks, and Support Vector Machines), and all possible combinations of sixteen color space components as different feature sets. We obtained 83.50% success in our experiments. As a result, the best performer combination of the classifier, the choice of the ROI, and the feature vector are demonstrated.
AB - Given outdoor vehicle images, we try to find an acceptable method chain that maximizes the vehicle color recognition success. Our aim is to determine the color of the vehicle located in a colored image and to make a decision among the chosen seven color classes. At this study, performances of different feature sets obtained by various color spaces and different classification methods are taken to account in order to improve the outdoor vehicle color recognition. Also, different Region of Interest (ROI) and feature vector construction methods are developed for gain better performance. We examined two ROI (smooth hood peace and semi front vehicle), three classification methods (K-Nearest Neighbors, Artificial Neural Networks, and Support Vector Machines), and all possible combinations of sixteen color space components as different feature sets. We obtained 83.50% success in our experiments. As a result, the best performer combination of the classifier, the choice of the ROI, and the feature vector are demonstrated.
KW - Classification method
KW - Color spaces
KW - Feature selection
KW - Histogram
KW - Outdoor color
KW - ROI selection
KW - Vehicle color recognition
UR - https://www.scopus.com/pages/publications/79952655912
M3 - Conference contribution
AN - SCOPUS:79952655912
SN - 9789604741953
T3 - Proc. of the 11th WSEAS Int. Conf. on Neural Networks, NN '10, Proceedings of the 11th WSEAS Int. Conf. on Evolutionary Computing, EC '10, Proc. of the 11th WSEAS Int. Conf. on Fuzzy Systems, FS '10
SP - 250
EP - 255
BT - Proc. of the 11th WSEAS Int. Conf. on Neural Networks, NN '10, Proceedings of the 11th WSEAS Int. Conf. on Evolutionary Computing, EC '10, Proc. of the 11th WSEAS Int. Conf. on Fuzzy Systems, FS '10
T2 - Proc. of the 11th WSEAS Int. Conf. on Neural Networks, NN '10, Proceedings of the 11th WSEAS Int. Conf. on Evolutionary Computing, EC '10, Proc. of the 11th WSEAS Int. Conf. on Fuzzy Systems, FS '10
Y2 - 13 June 2010 through 15 June 2010
ER -