The comparison data of match rate between the traditional SIFT feature matching method and the proposed method.</p
Comparison of performance obtained by our approach with other state-of-the-art algorithms.</p
Face matching performance as a function of survey condition for Study 2 (threshold = 0.5).</p
International audienceUnlike the matching strategy of minimizing dissimilarity measure between descr...
<p>Comparison of accuracy rate of different features extracted with classification algorithms.</p
<p>Comparison of classification rates between the proposed method and other methods.</p
Comparison of the classification accuracies of different algorithms and different feature fusion met...
<p>Comparison of the average precision rates, recall rates and F1 values for the different classific...
<p>Comparison of the computation speed for the Improved SIFT and the Original SIFT.</p
Comparison of results obtained with sieving method and image-based method (C = 0.85) (%).</p
<p>Comparison of the recognition rates of the proposed method with some popular classifiers in the l...
Performance comparison of different feature selection techniques on EN dataset in group AB.</p
Comparison of the user click-through rate of best match versus the previous TF–IDF method and the de...
Comparison of the proposed method with other conventional methods for throughput of each class.</p
<p>Comparison between restricted and adjusted matching criteria and respective results.</p
Comparison of accuracy and feature dimension under different methods based on tree.</p
Comparison of performance obtained by our approach with other state-of-the-art algorithms.</p
Face matching performance as a function of survey condition for Study 2 (threshold = 0.5).</p
International audienceUnlike the matching strategy of minimizing dissimilarity measure between descr...
<p>Comparison of accuracy rate of different features extracted with classification algorithms.</p
<p>Comparison of classification rates between the proposed method and other methods.</p
Comparison of the classification accuracies of different algorithms and different feature fusion met...
<p>Comparison of the average precision rates, recall rates and F1 values for the different classific...
<p>Comparison of the computation speed for the Improved SIFT and the Original SIFT.</p
Comparison of results obtained with sieving method and image-based method (C = 0.85) (%).</p
<p>Comparison of the recognition rates of the proposed method with some popular classifiers in the l...
Performance comparison of different feature selection techniques on EN dataset in group AB.</p
Comparison of the user click-through rate of best match versus the previous TF–IDF method and the de...
Comparison of the proposed method with other conventional methods for throughput of each class.</p
<p>Comparison between restricted and adjusted matching criteria and respective results.</p
Comparison of accuracy and feature dimension under different methods based on tree.</p
Comparison of performance obtained by our approach with other state-of-the-art algorithms.</p
Face matching performance as a function of survey condition for Study 2 (threshold = 0.5).</p
International audienceUnlike the matching strategy of minimizing dissimilarity measure between descr...
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