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This work presents a novel Content-Based Video Retrieval approach in order to cope with the semantic gap challenge by means of latent topics. Firstly, a supervised topic model is proposed to transform the classical retrieval approach into a class discovery problem. Subsequently, a new probabilistic ranking function is deduced from that model to tackle the semantic gap between low-level features and high-level concepts. Finally, a shortterm relevance feedback scheme is defined where queries candoi:10.1016/j.patcog.2015.09.007 fatcat:c7gqy4xgabdv3neij54znms3wa