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.
| Author: | Thomas GreifGND, Eva HörsterGND, Rainer LienhartGND |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-9263 |
| Frontdoor URL | https://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 Maschinelles Lernen und Maschinelles Sehen | |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
