Retrieving Relevant and Diverse Movie Clips Using the MFVCD-7K Multifaceted Video Clip Dataset

Multimedia search is an emerging area in information retrieval (IR) and recommender systems (RS) research. However, there is a lack of standardized audiovisual datasets that include rich content descriptors, which are a necessity in content-based IR and RS. The contributions of this paper are twofold: First, we present a new multimedia dataset of movie clips, named MFVCD-7K Multifaceted Video Clip Dataset, that comes with low-level and semantic multimodal descriptions of their content (textual, audio, and visual). In addition, we showcase the use of this dataset for a novel content-based video clip retrieval and result diversification task we introduce. We investigate baseline algorithms for retrieval and diversification, and provide experimental results according to relevance and diversity measures. We believe that both dataset and baseline results constitute an important asset for the IR, RS, and multimedia communities.