Concept-based near-duplicate video clip detection for novelty re-ranking of web video search results

Abstract

State-of-the-art near-duplicate video clip (NDVC) detection for novelty re-ranking uses nonsemantic low-level features (color/texture) to detect and eliminate "content-based NDVC" and increases content level novelty in the top results. However, humans may perceive a video as near duplicate from a semantic perspective as well. In this paper, we propose concept-based near-duplicate video clip (CBNDVC) detection technique for novelty re-ranking. We identify "semantic NDVC", making use of the semantic features (events/concepts) and re-rank the top results to increase the content as well as semantic novelty. Videos are represented as a multivariate time series of confidence values of relevant concepts and thereafter discovery of CBNDVC clusters is achieved by conceptual clustering. Obtained results show higher precision and recall from the user's perspective. © Springer-Verlag 2011.