Correlated Topic Models for Image Retrieval

Correlated Topic Models for Image Retrieval

erschienen 10.07.08 Technical Report, Institute of Computer Science, University of Augsburg, July 2008

ABSTRACT

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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