@inproceedings{d33178497dff45df949579816be30bf1,
title = "Evaluating feature combination in object classification",
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.",
author = "Jian Hou and Zhang, \{Bo Ping\} and Qi, \{Nai Ming\} and Yong Yang",
year = "2011",
doi = "10.1007/978-3-642-24031-7\_60",
language = "英语",
isbn = "9783642240300",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 2",
pages = "597--606",
booktitle = "Advances in Visual Computing - 7th International Symposium, ISVC 2011, Proceedings",
address = "德国",
edition = "PART 2",
note = "7th International Symposium on Visual Computing, ISVC 2011 ; Conference date: 26-09-2011 Through 28-09-2011",
}