Continuous visual vocabulary modelsfor pLSA-based scene recognition

Eva Hörster, Rainer Lienhart, Malcolm Slaney
2008 Proceedings of the 2008 international conference on Content-based image and video retrieval - CIVR '08  
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
more » ... ent 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.
doi:10.1145/1386352.1386395 dblp:conf/civr/HorsterLS08 fatcat:m6lz4trytbf6xowgm4fy2btzca