An interactive video retrieval approach based on latent topics

Abstract

The huge collections of unconstrained videos have amplified the so-called semantic gap for content-based video retrieval. Therefore, new efficient approaches with higher generalisation power are needed. In this work, we present an interactive video retrieval approach based on latent topics to cope with the semantic gap in an efficient way. A supervised Symmetric extension of probabilistic Latent Semantic Analysis model is presented (sSpLSA). Then, this model is adapted to an on-line interactive information retrieval problem and it is applied to a video retrieval framework based on explicit short-term Relevance Feedback (RF) where queries are inside the database. Finally, several retrieval simulations using the Consumer Columbia Video (CCV) database are performed to compare the proposed approach with a distance-based RF baseline. © 2013 Springer-Verlag.