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Classifying Images at Scene Level: Comparing Global and Local Descriptors

  • Christian Hentschel
  • Sebastian Gerke
  • Eugene Mbanya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7836)

Abstract

In this paper we compare two state-of-the-art approaches for image classification. The first approach follows the Bag-of-Keypoints method for classifying images based on local image pattern frequency distribution. The second approach computes the gist of an image by computing global image statistics. Both approaches are explained in detail and their performance is compared using a subset of images taken from the ImageClef 2011 PhotoAnnotation task. The images were selected based on the assumption they could be better described using global features. Results show that while Bag-of-Keypoints-like classification performs better even for global concepts the classification accuracy of the global descriptor remains acceptable at a much smaller computational footprint.

Keywords

Support Vector Machine Feature Vector Local Descriptor Mean Average Precision Video Retrieval 
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 2013

Authors and Affiliations

  • Christian Hentschel
    • 1
  • Sebastian Gerke
    • 1
  • Eugene Mbanya
    • 1
  1. 1.Fraunhofer Institute for TelecommunicationsHeinrich Hertz InstituteGermany

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