Recent research trends in Content-based Video Retrieval have shown topic models as an effective tool to deal
with the semantic gap challenge. In this scenario, this paper has a dual target: (1) it is aimed at studying how
the use of different topic models (pLSA, LDA and FSTM) affects video retrieval performance; (2) a novel incremental
topic model (IpLSA) is presented in order to cope with incremental scenarios in an effective and efficient
way. A comprehensive comparison among these four topic models using two different retrieval systems and two
reference benchmarking video databases is provided. Experiments revealed that pLSA is the best model in sparse
conditions, LDA tend to outperform the rest of the models in a dense space and IpLSA is able to work properly in
both cases
