Video Content Representation by Incremental Non-Negative Matrix Factorization

Abstract

Nonnegative matrix factorization (NMF) is a powerful decomposition tool which has been used in several content representation applications recently. However, there are some difficulties in implementing NMF in on-line video applications. This paper introduces an incremental NMF (INMF) without deviating from conventional NMFs main objective function, which is minimizing the reconstruction error. The proposed algorithm is capable of modeling dynamic content of the video; thus controls contribution of the subsequent observations to the NMF representation properly. It is shown that the INMF preserves additive, parts-based representation capability of the NMF with a low computational load while offering dimension reduction. Experimental results are given to compare the reconstruction performances of the conventional and incremental NMF. In addition, video scene change detection and dynamic video content representation by INMF are investigated. Test results demonstrate that the INMF can be used as a powerful on-line factorization tool.