Thumbnail Tensor—A Method for Multidimensional Data Streams Clustering with an Efficient Tensor Subspace Model in the Scale-Space

Abstract

In this paper an efficient method for signal change detection in multidimensional data streams is proposed. A novel tensor model is suggested for input signal representation and analysis. The model is built from a part of the multidimensional stream by construction of the representing orthogonal tensor subspaces, computed with the higher-order singular value decomposition (HOSVD). Parts of the input data stream from successive time windows are then compared with the model, which is either updated or rebuilt, depending on the result of the proposed statistical inference rule. Due to processing of the input signal tensor in the scale-space, the thumbnail like output is obtained. Because of this, the method is called a thumbnail tensor. The method was experimentally verified on annotated video databases and on real underwater sequences. The results show a significant improvement over other methods both in terms of accuracy as well as in speed of operation time.