論文

査読有り
2009年

A procedure of adaptive kernel combination with kernel-target alignment for object classification

CIVR 2009 - Proceedings of the ACM International Conference on Image and Video Retrieval
  • Motoaki Kawanabe
  • ,
  • Shinichi Nakajima
  • ,
  • Alexander Binder

開始ページ
168
終了ページ
174
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1145/1646396.1646427
出版者・発行元
ACM

In order to achieve good performance in object classification problems, it is necessary to combine information from various image features. Because the large margin classifiers are constructed based on similarity measures between samples called kernels, finding appropriate feature combinations boils down to designing good kernels among a set of candidates, for example, positive mixtures of predetermined base kernels. There are a couple of ways to determine the mixing weights of multiple kernels: (a) uniform weights, (b) a brute force search over a validation set and (c) multiple kernel learning (MKL). MKL is theoretically and technically very attractive, because it learns the kernel weights and the classifier simultaneously based on the margin criterion. However, we often observe that the support vector machine (SVM) with the average kernel works at least as good as MKL. In this paper, we propose as an alternative, a two-step approach: at first, the kernel weights are determined by optimizing the kernel-target alignment score and then the combined kernel is used by the standard SVM with a single kernel. The experimental results with the VOC 2008 data set [8] show that our simple procedure outperforms the average kernel and MKL. Copyright 2009 ACM.

リンク情報
DOI
https://doi.org/10.1145/1646396.1646427
URL
http://doi.acm.org/10.1145/1646396.1646427
URL
http://dblp.uni-trier.de/db/conf/civr/civr2009.html#conf/civr/KawanabeNB09
ID情報
  • DOI : 10.1145/1646396.1646427
  • DBLP ID : conf/civr/KawanabeNB09
  • SCOPUS ID : 74049144877

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