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Feature Analysis for Object and Scene Categorization

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Innovations in Intelligent Image Analysis

Part of the book series: Studies in Computational Intelligence ((SCI,volume 339))

Abstract

Feature extraction and selection has always been an interesting issue for pattern recognition tasks. There have been numerous feature schemes proposed and empirically validated for image scene and object categorization problems, no matter it is for general-purposed applications such as image retrieval, or for specific domains such as medical image analysis. On the other hand, there are few attempts in assessing the effectiveness of these features using machine learning methods of feature analysis. We review some recent advances in feature selection and investigate the use of feature analysis and selection in two case studies. Our aim is to demonstrate that feature selection is indispensable in providing clues for finding good feature combination schemes and building compact and effective classifiers that produce much improved performance.

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Author information

Authors and Affiliations

  1. Department of Information Science, University of Otago, PO Box 56, Dunedin, 9054, New Zealand

    Jeremiah D. Deng

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  1. Jeremiah D. Deng
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Editors and Affiliations

  1. Deputy Director for Scientific Researches Institute of Informatics, Wroclaw University of Technology , Wyb. Wyspianskiego 27, 51-370, Wroclaw, Poland

    Halina Kwaśnicka

  2. Founding Director of the KES Centre School of Electrical and Information Engineering, Knowledge-Based Engineering University of South Australia Adelaide , Mawson Lakes Campus, 5095, South Australia, SA, Australia

    Lakhmi C. Jain

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Deng, J.D. (2011). Feature Analysis for Object and Scene Categorization. In: Kwaśnicka, H., Jain, L.C. (eds) Innovations in Intelligent Image Analysis. Studies in Computational Intelligence, vol 339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17934-1_10

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  • DOI: https://doi.org/10.1007/978-3-642-17934-1_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17933-4

  • Online ISBN: 978-3-642-17934-1

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