Comparison of performance obtained by our approach with other state-of-the-art algorithms.</p
Performance comparison of the proposed method with state-of-the-art methods on the Wang-B dataset.</...
<p>Comparison of the average classification accuracies of different algorithms for different numbers...
<p>Comparison of accuracy rate of different features extracted with classification algorithms.</p
<p>Comparison of the proposed algorithm with the state of the art methods available in literature.</...
<p>Comparison of our approach and counterpart algorithms in terms of running time (<i>s</i>).</p
<p>Performance comparison of the first experiment (results of our proposed algorithm are in bold).</...
<p>The comparison of computational efficiency of each algorithm on the test functions.</p
<p>Performance comparison of the second experiment (results of our proposed algorithm are in bold).<...
Comparing performance of the proposed methods built with different number of individual models.</p
<p>Comparing the performance of the proposed method with other existing methods.</p
Comparison of the classification performance by the proposed network and other methods.</p
Performance comparison of the proposed method with state-of-the-art methods on the Caltech-256 datas...
Comparison of the classification accuracies of different algorithms and different feature fusion met...
Performance comparison of the proposed method with state-of-the-art methods on the OT Scene dataset....
Performance statistics of the tested algorithms at different activity levels.</p
Performance comparison of the proposed method with state-of-the-art methods on the Wang-B dataset.</...
<p>Comparison of the average classification accuracies of different algorithms for different numbers...
<p>Comparison of accuracy rate of different features extracted with classification algorithms.</p
<p>Comparison of the proposed algorithm with the state of the art methods available in literature.</...
<p>Comparison of our approach and counterpart algorithms in terms of running time (<i>s</i>).</p
<p>Performance comparison of the first experiment (results of our proposed algorithm are in bold).</...
<p>The comparison of computational efficiency of each algorithm on the test functions.</p
<p>Performance comparison of the second experiment (results of our proposed algorithm are in bold).<...
Comparing performance of the proposed methods built with different number of individual models.</p
<p>Comparing the performance of the proposed method with other existing methods.</p
Comparison of the classification performance by the proposed network and other methods.</p
Performance comparison of the proposed method with state-of-the-art methods on the Caltech-256 datas...
Comparison of the classification accuracies of different algorithms and different feature fusion met...
Performance comparison of the proposed method with state-of-the-art methods on the OT Scene dataset....
Performance statistics of the tested algorithms at different activity levels.</p
Performance comparison of the proposed method with state-of-the-art methods on the Wang-B dataset.</...
<p>Comparison of the average classification accuracies of different algorithms for different numbers...
<p>Comparison of accuracy rate of different features extracted with classification algorithms.</p
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