Correlated Topic Models for Image Retrieval

  • In our previous work [4] we have shown that the representation of images by the Latent Dirichlet Allocation (LDA) model combined with an appropriate similarity measure is suitable for performing large-scale image retrieval in a realworld database. The LDA model, however, relies on the assumption that all topics are independent of each other – something that is obviously not true in most cases. In this work we study a recently proposed model, the Correlated Topic Model (CTM) [1], in the context of large-scale image retrieval. This approach is able to explicitly model such correlations of topics. We experimentally evaluate the proposed retrieval approach on a real-world large-scale database consisting of more than 246,000 images and compare the performance to related approaches.

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Metadaten
Author:Thomas GreifGND, Eva HörsterGND, Rainer LienhartGND
URN:urn:nbn:de:bvb:384-opus4-9263
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/1077
Series (Serial Number):Reports / Technische Berichte der Fakultät für Angewandte Informatik der Universität Augsburg (2008-09)
Type:Report
Language:English
Publishing Institution:Universität Augsburg
Release Date:2008/07/10
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