Abstract
Nonnegative Matrix Factorization (NMF) has already found many applications in image processing and data analysis, including classification, clustering, feature extraction, pattern recognition, and blind image separation. In the paper, we extend the selected NMF algorithms by taking into account local smoothness properties of source images. Our modifications are related with incorporation of the Gibbs prior, which is well-known in many tomographic image reconstruction applications, to a underlying blind image separation model. The numerical results demonstrate the improved performance of the proposed methods in comparison to the standard NMF algorithms.
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Keywords
- Monte Carlo
- Blind Source Separation
- Nonnegative Matrix Factorization
- Alternate Little Square
- Pattern Recognition Letter
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Zdunek, R., Cichocki, A. (2008). Blind Image Separation Using Nonnegative Matrix Factorization with Gibbs Smoothing. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_54
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DOI: https://doi.org/10.1007/978-3-540-69162-4_54
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