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Scene categorization using topic model based hierarchical conditional random fields
V. Garg, E. Hassan, , M. Gopal
Published in
Volume: 6744 LNCS
Pages: 206 - 212
We propose a novel hierarchical framework for scene categorization. The scene representation is defined by latent topics extracted by Latent Dirichlet Allocation. The interaction of these topics across scene categories is learned by probabilistic graphical modelling. We use Conditional Random Fields in a hierarchical setting for discovering the global context of these topics. The learned random fields are further used for categorization of a new scene. The experimental results of the proposed framework is presented on standard datasets and on image collection obtained from the internet. © 2011 Springer-Verlag Berlin Heidelberg.
About the journal
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)