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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 threedoi:10.1145/1386352.1386395 dblp:conf/civr/HorsterLS08 fatcat:m6lz4trytbf6xowgm4fy2btzca