論文

査読有り
2010年

Enhancing Recognition of Visual Concepts with Primitive Color Histograms via Non-sparse Multiple Kernel Learning

MULTILINGUAL INFORMATION ACCESS EVALUATION II: MULTIMEDIA EXPERIMENTS, PT II
  • Alexander Binder
  • ,
  • Motoaki Kawanabe

6242
開始ページ
269
終了ページ
276
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
10.1007/978-3-642-15751-6_33
出版者・発行元
SPRINGER-VERLAG BERLIN

In order to achieve good performance in image annotation tasks, it is necessary to combine information from various image features. In recent competitions on photo annotation, many groups employed the bag-of-words (BoW) representations based on the SIFT descriptors over various color channels. In fact, it has been observed that adding other less informative features to the standard BoW degrades recognition performances. In this contribution, we will show that even primitive color histograms can enhance the standard classifiers in the ImageCLEF 2009 photo annotation task, if the feature weights are tuned optimally by non-sparse multiple kernel learning (MKL) proposed by Kloft et al.. Additionally, we will propose a sorting scheme of image subregions to deal with spatial variability within each visual concept.

Web of Science ® 被引用回数 : 3

リンク情報
DOI
https://doi.org/10.1007/978-3-642-15751-6_33
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000286416400033&DestApp=WOS_CPL
URL
http://dblp.uni-trier.de/db/conf/clef/clef2009-2.html#conf/clef/BinderK09
ID情報
  • DOI : 10.1007/978-3-642-15751-6_33
  • ISSN : 0302-9743
  • DBLP ID : conf/clef/BinderK09
  • Web of Science ID : WOS:000286416400033

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