Feature Extraction and a Database Strategy for Video Fingerprinting
VISUAL '02 Proceedings of the 5th International Conference on Recent Advances in Visual Information Systems
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Nonsmooth Nonnegative Matrix Factorization (nsNMF)
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Rapid scene analysis on compressed video
IEEE Transactions on Circuits and Systems for Video Technology
Nonlinear nonnegative matrix factorization based on Mercer kernel construction
Pattern Recognition
Background modeling via incremental maximum margin criterion
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Incremental face recognition for large-scale social network services
Pattern Recognition
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In this paper we introduce an incremental non-negative matrix factorization (INMF) scheme in order to overcome the difficulties that conventional NMF has in online processing of large data sets. The proposed scheme enables incrementally updating its factors by reflecting the influence of each observation on the factorization appropriately. This is achieved via a weighted cost function which also allows controlling the memorylessness of the factorization. Unlike conventional NMF, with its incremental nature and weighted cost function the INMF scheme successfully utilizes adaptability to dynamic data content changes with a lower computational complexity. Test results reported for two video applications, namely background modeling in video surveillance and clustering, demonstrate that INMF is capable of online representing data content while reducing dimension significantly.