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• W2407901846 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." @default.
• W2407901846 created "2016-06-24" @default.
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• W2407901846 date "2016-07-05" @default.
• W2407901846 modified "2023-09-25" @default.
• W2407901846 title "Vehicle logo recognition by spatial-SIFT combined with logistic regression" @default.
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