Efficient Spatio-Temporal Edge Descriptor

Concept-based video retrieval is a developing area of current multimedia content analysis research. The use of spatio-temporal descriptors in content-based video retrieval has always seemed like a promising way to bridge the semantic gap problem in ways that typical visual retrieval methods cannot. In this paper we propose a spatio-temporal descriptor called ST-MP7EH which can address some of the challenges encountered in practical systems and we present our experimental results in support of our participation at TRECVid 2011 Semantic Indexing. This descriptor combines the MPEG-7 Edge Histogram descriptor with motion information and is designed to be computationally efficient, scalable and highly parallel. We show that our descriptor performs well in SVM classification compared to a baseline spatio-temporal descriptor, which is inspired by some of the state-of-the-art systems that make the top lists of TRECVid. We highlight the importance of the temporal component by comparing to the initial edge histogram descriptor and the potential of feature fusion with other classifiers.