Model-based similarity estimation of multidimensional temporal sequences
Content-based queries in multimedia sequence databases where information is sequential is a tough issue, especially when dealing with large-scale applications. One of the key points is similarity estimation between a query sequence and elements of the database. In this paper, we investigate two ways to compare multimedia sequences, onethat comes from the literaturebeing computed in the feature space while the other one is computed in a model space, leading to a representation less sensitive to noise. We compare these approaches by testing them on a real audio dataset, which points out the utility of working in the model space.
multidimensional feature sequences support vector regression temporal aspects similarity estimation in a model space