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Multi-class Object Detection with Hough Forests Using Local Histograms of Visual Words

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Computer Analysis of Images and Patterns (CAIP 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6854))

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Abstract

Multi-class object detection is a promising approach for reducing the processing time of object recognition tasks. Recently, random Hough forests have been successfully used for single-class object detection. In this paper, we present an extension of random Hough forests for the purpose of multi-class object detection and propose local histograms of visual words as appropriate features. Experimental results for the Caltech-101 test set demonstrate that the performance of the proposed approach is almost as good as the performance of a single-class object detector, even when detecting a large number of 24 object classes at a time.

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Author information

Authors and Affiliations

  1. Department of Mathematics & Computer Science, University of Marburg, Hans-Meerwein-Str. 3, D-35032, Marburg, Germany

    Markus Mühling, Ralph Ewerth, Bing Shi & Bernd Freisleben

Authors
  1. Markus Mühling
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  2. Ralph Ewerth
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  3. Bing Shi
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  4. Bernd Freisleben
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Editor information

Editors and Affiliations

  1. Dpto. Matematica Aplicada I, Escuaela Técnica Superior de Ingeniería Informática, Universite de Sevilla, Avda. Reina Mercedes, s/n, 41012, Sevilla, Spain

    Pedro Real

  2. Departamento de Matemática Aplicada I, Escuela Técnica Superior de Ingeniería Informática, University of Seville, Avenida Reina Mercedes s/n, 41012, Sevilla, Spain

    Daniel Diaz-Pernil  & Helena Molina-Abril  & 

  3. Departamento de Didáctica de la Mathemática y de las CC.Experimentales, Universidad del País Vasco-Esukal Herriko Unibertsitatea, Escuela Universitaria de Magisterio, Ramón y Cajal, 72, 48014, Bilbao (Bizcaia), Spain

    Ainhoa Berciano

  4. Institute of Computer Graphics and Algorithms, Pattern Recognition and Image Processing Group, Vienna University of Technology, Favoritenstraße 9/186-3, 1040, Vienna, Austria

    Walter Kropatsch

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© 2011 Springer-Verlag Berlin Heidelberg

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Mühling, M., Ewerth, R., Shi, B., Freisleben, B. (2011). Multi-class Object Detection with Hough Forests Using Local Histograms of Visual Words. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23672-3_47

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  • DOI: https://doi.org/10.1007/978-3-642-23672-3_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23671-6

  • Online ISBN: 978-3-642-23672-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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