Abstract
Feature combination is used in object classification to combine the strength of multiple complementary features and yield a more powerful feature. While some work can be found in literature to calculate the weights of features, the selection of features used in combination is rarely touched. Different researchers usually use different sets of features in combination and obtain different results. It’s not clear to which degree the superior combination results should be attributed to the combination methods and not the carefully selected feature sets. In this paper we evaluate the impact of various feature-related factors on feature combination performance. Specifically, we studied the combination of various popular descriptors, kernels and spatial pyramid levels through extensive experiments on four datasets of diverse object types. As a result, we provide some empirical guidelines on designing experimental setups and combination algorithms in object classification.
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Hou, J., Zhang, BP., Qi, NM., Yang, Y. (2011). Evaluating Feature Combination in Object Classification. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6939. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24031-7_60
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DOI: https://doi.org/10.1007/978-3-642-24031-7_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24030-0
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