'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
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