Vehicle logo recognition by spatial-SIFT combined with logistic regression

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.