Abstract
Non-negative matrix factorization (NMF) is an efficient local feature extraction algorithm of natural images. To extract well features of natural images, some sparse variants of NMF, such as sparse NMF (SNMF), local NMF (LNMF), and NMF with sparseness constraints (NMFSC), have been explored. Here, used face images and palmprint images as test images, and considered different number of feature basis dimension, the validity of feature extraction using SNMF, LNMF and NMFSC is testified. Experimental results demonstrate that the level of feature extraction of LNMF is the best, and that of NMFSC is the worse, which also provides some guidance to use different NMF based algorithm in image processing task, and our task in this paper behave certain theory research meaning and application in practice.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(21), 788–791 (1999)
Heiler, M., Schnörr, C.: Learning sparse representations by non-negative matrix factorization and sequential come programming. Journal of Machine Learning Research 7, 1385–1407 (2006)
Li Stan, Z., Hou, X.W., Zhang, H.J., et al.: Learning spatially localized, parts-based representation. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, USA, vol. 1, pp. 207–212 (2001)
Stadlthanner, K., Theis, F.J., Lang, E.W., Tomé, A.M., Puntonet, C.G., Vilda, P.G., Langmann, T., Schmitz, G.: Sparse Nonnegative Matrix Factorization Applied to Microarray Data Sets. In: Rosca, J.P., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 254–261. Springer, Heidelberg (2006)
Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research 5, 1427–1469 (2004)
Li, L., Zhang, Y.J.: A survey on algorithms of non-negative matrix factorization. Acta Electronica Sinica 36(4), 737–743 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shang, L., Zhou, Y., Chen, J., Huai, Wj. (2012). Nature Image Feature Extraction Using Several Sparse Variants of Non-negative Matrix Factorization Algorithm. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-31362-2_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31361-5
Online ISBN: 978-3-642-31362-2
eBook Packages: Computer ScienceComputer Science (R0)