Comparing Local Feature Descriptors in pLSA-Based Image Models

  • 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.

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Metadaten
Author:Eva HörsterGND, Thomas GreifGND, Rainer LienhartGND, Malcolm Slaney
URN:urn:nbn:de:bvb:384-opus4-7562
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/900
Series (Serial Number):Reports / Technische Berichte der Fakultät für Angewandte Informatik der Universität Augsburg (2007-15)
Type:Report
Language:English
Publishing Institution:Universität Augsburg
Release Date:2008/06/16
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Multimedia und Maschinelles Sehen
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik