Continuous Visual Vocabulary Models for pLSA-Based Scene Recognition

  • Topic models such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) have been shown to perform well in various image content analysis tasks. However, due to the origin of these models from the text domain, almost all prior work uses discrete vocabularies even when applied in the image domain. Thus in these works the continuous local features used to describe an image need to be quantized to fit the model. In this work we will propose and evaluate three different extensions to the pLSA framework so that words are modeled as continuous feature vector distributions rather than crudely quantized high-dimensional descriptors. The performance of these continuous vocabulary models are compared in an automatic scene recognition task. Our experiments clearly show that the continuous approaches outperform the standard pLSA model. In this paper all required equations for parameter estimation and inference are given for each of the three models.

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Author:Eva HörsterGND, Rainer LienhartGND, Malcolm Slaney
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Series (Serial Number):Reports / Technische Berichte der Fakultät für Angewandte Informatik der Universität Augsburg (2008-05)
Publishing Institution:Universität Augsburg
Release Date:2008/04/24
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Maschinelles Lernen und Maschinelles Sehen
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik