Comparing Local Feature Descriptors in pLSA-Based Image Models

  • Eva Hörster
  • Thomas Greif
  • Rainer Lienhart
  • Malcolm Slaney
Conference paper

DOI: 10.1007/978-3-540-69321-5_45

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5096)
Cite this paper as:
Hörster E., Greif T., Lienhart R., Slaney M. (2008) Comparing Local Feature Descriptors in pLSA-Based Image Models. In: Rigoll G. (eds) Pattern Recognition. DAGM 2008. Lecture Notes in Computer Science, vol 5096. Springer, Berlin, Heidelberg

Abstract

Probabilistic models with hidden variables such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) have recently become popular for solving several image content analysis tasks. In this work we will use a pLSA model to represent images for performing scene classification. We evaluate the influence of the type of local feature descriptor in this context and compare three different descriptors. Moreover we also examine three different local interest region detectors with respect to their suitability for this task. Our results show that two examined local descriptors, the geometric blur and the self-similarity feature, outperform the commonly used SIFT descriptor by a large margin.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Eva Hörster
    • 1
  • Thomas Greif
    • 1
  • Rainer Lienhart
    • 1
  • Malcolm Slaney
    • 2
  1. 1.Multimedia Computing LabUniversity of AugsburgGermany
  2. 2.Yahoo! ResearchSanta ClaraUSA

Personalised recommendations