TY - GEN
T1 - Vehicle logo recognition by spatial-SIFT combined with logistic regression
AU - Chen, Ruilong
AU - Hawes, Matthew
AU - Mihaylova, Lyudmila
AU - Xiao, Jingjing
AU - Liu, Wei
N1 - Publisher Copyright:
© 2016 ISIF.
PY - 2016/7
Y1 - 2016/7
N2 - An efficient recognition framework requires both good feature representation and effective classification methods. This paper proposes such a framework based on a spatial Scale Invariant Feature Transform (SIFT) combined with a logistic regression classifier. The performance of the proposed framework is compared to that of state-of-the-art methods based on the Histogram of Orientation Gradients, SIFT features, Support Vector Machine and K-Nearest Neighbours classifiers. By testing with the largest vehicle logo data-set, it is shown that the proposed framework can achieve a classification accuracy of 99:93%, the best among all studied methods. Moreover, the proposed framework shows robustness when noise is added in both training and testing images.
AB - An efficient recognition framework requires both good feature representation and effective classification methods. This paper proposes such a framework based on a spatial Scale Invariant Feature Transform (SIFT) combined with a logistic regression classifier. The performance of the proposed framework is compared to that of state-of-the-art methods based on the Histogram of Orientation Gradients, SIFT features, Support Vector Machine and K-Nearest Neighbours classifiers. By testing with the largest vehicle logo data-set, it is shown that the proposed framework can achieve a classification accuracy of 99:93%, the best among all studied methods. Moreover, the proposed framework shows robustness when noise is added in both training and testing images.
UR - http://www.scopus.com/inward/record.url?scp=84992083416&partnerID=8YFLogxK
M3 - Conference article published in proceeding or book
AN - SCOPUS:84992083416
T3 - FUSION 2016 - 19th International Conference on Information Fusion, Proceedings
SP - 1228
EP - 1235
BT - FUSION 2016 - 19th International Conference on Information Fusion, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th International Conference on Information Fusion, FUSION 2016
Y2 - 5 July 2016 through 8 July 2016
ER -