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Efficient Object Pixel-Level Categorization Using Bag of Features

  • David Aldavert
  • Arnau Ramisa
  • Ricardo Toledo
  • Ramon Lopez de Mantaras
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5875)

Abstract

In this paper we present a pixel-level object categorization method suitable to be applied under real-time constraints. Since pixels are categorized using a bag of features scheme, the major bottleneck of such an approach would be the feature pooling in local histograms of visual words. Therefore, we propose to bypass this time-consuming step and directly obtain the score from a linear Support Vector Machine classifier. This is achieved by creating an integral image of the components of the SVM which can readily obtain the classification score for any image sub-window with only 10 additions and 2 products, regardless of its size. Besides, we evaluated the performance of two efficient feature quantization methods: the Hierarchical K-Means and the Extremely Randomized Forest. All experiments have been done in the Graz02 database, showing comparable, or even better results to related work with a lower computational cost.

Keywords

Visual Word Integral Image Equal Error Rate Linear Support Vector Machine Vocabulary Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • David Aldavert
    • 1
  • Arnau Ramisa
    • 2
  • Ricardo Toledo
    • 1
  • Ramon Lopez de Mantaras
    • 2
  1. 1.Computer Vision Center (CVC), Dept. Ciències de la ComputacióUniversitat Autònoma de Barcelona (UAB)BellaterraSpain
  2. 2.Artificial Intelligence Research Institute (IIIA-CSIC), Campus de la UABBellaterraSpain

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