Comparing feature matching for object categorization in video surveillance

R.G.J. Wijnhoven, P.H.N. With, de

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In this paper we consider an object categorization system using local HMAX features. Two feature matching techniques are compared: the MAX technique, originally proposed in the HMAX framework, and the histogram technique originating from Bag-of-Words literature. We have found that each of these techniques have their own field of operation. The histogram technique clearly outperforms the MAX technique with 5-15% for small dictionaries up to 500-1,000 features, favoring this technique for embedded (surveillance) applications. Additionally, we have evaluated the influence of interest point operators in the system. A first experiment analyzes the effect of dictionary creation and has showed that random dictionaries outperform dictionaries created from Hessian-Laplace points. Secondly, the effect of operators in the dictionary matching stage has been evaluated. Processing all image points outperforms the point selection from the Hessian-Laplace operator.
Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2009), 28 September - 2 October 2009, Bordeaux, France
EditorsJ. Blanc-Talon, W. Philips, D. Popescu, P. Scheunders
Place of PublicationBerlin
ISBN (Print)978-3-642-04696-4
Publication statusPublished - 2009

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


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