jmlr jmlr2005 jmlr2005-65 jmlr2005-65-reference knowledge-graph by maker-knowledge-mining

65 jmlr-2005-Separating a Real-Life Nonlinear Image Mixture


Source: pdf

Author: Luís B. Almeida

Abstract: When acquiring an image of a paper document, the image printed on the back page sometimes shows through. The mixture of the front- and back-page images thus obtained is markedly nonlinear, and thus constitutes a good real-life test case for nonlinear blind source separation. This paper addresses a difficult version of this problem, corresponding to the use of “onion skin” paper, which results in a relatively strong nonlinearity of the mixture, which becomes close to singular in the lighter regions of the images. The separation is achieved through the MISEP technique, which is an extension of the well known INFOMAX method. The separation results are assessed with objective quality measures. They show an improvement over the results obtained with linear separation, but have room for further improvement. Keywords: ICA, blind source separation, nonlinear mixtures, nonlinear separation, image mixture, image separation


reference text

L. B. Almeida. Faster training in nonlinear ICA using MISEP. In Proceedings of the International Workshop on Independent Component Analysis and Blind Signal Separation, pages 113–118, Nara, Japan, 2003a. URL http://www.lx.it.pt/˜lbalmeida/papers/AlmeidaICA03.pdf. L. B. Almeida. MISEP – Linear and nonlinear ICA based on mutual information. Journal of Machine Learning Research, 4:1297–1318, 2003b. URL http://www.jmlr.org/papers/volume4/almeida03a/almeida03a.pdf. L. B. Almeida and M. Faria. Separating a real-life nonlinear mixture of images. In Carlos G. Puntonet and Alberto Prieto, editors, Independent Component Analysis and Blind Signal Separation (Proc. ICA’2004), number 3195 in Lecture Notes in Artificial Intelligence, pages 729–736, Granada, Spain, 2004. Springer-Verlag. URL http://www.lx.it.pt/˜lbalmeida/papers/AlmeidaICA04.pdf. S. Amari, A. Cichocki, and H. H. Yang. A new learning algorithm for blind signal separation. In David Touretzky, Michael Mozer, and Mark Hasselmo, editors, Advances in Neural Information Processing Systems 8, pages 757–763. MIT Press, 1996. URL http://www.islab.brain.riken.go.jp/˜amari/pub/acyNIPS95.ps.Z. A. Bell and T. Sejnowski. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7:1129–1159, 1995. URL ftp://ftp.cnl.salk.edu/pub/tony/bell.blind.ps. G. Burel. Blind separation of sources: A nonlinear neural algorithm. Neural Networks, 5(6):937– 947, 1992. P. Comon. Independent component analysis – a new concept? Signal Processing, 36:287–314, 1994. G. Darmois. Analyse g´ n´ rale des liaisons stochastiques. Revue de l’Institut International de e e Statistique, 21:2–8, 1953. G. Deco and W. Brauer. Nonlinear higher-order statistical decorrelation by volume-conserving neural architectures. Neural Networks, 8:525–535, 1995. M. Haritopoulos, H. Yin, and N. Allinson. Image denoising using SOM-based nonlinear independent component analysis. Neural Networks, 15(8–9):1085–1098, 2002. S. Harmeling, A. Ziehe, M. Kawanabed, and K.-R. M¨ ller. u Kernel-based nonlinear blind source separation. Neural Computation, 15:1089–1124, 2003. URL http://ida.first.fraunhofer.de/˜harmeli/papers/article_on_ktdsep.pdf. A. Hyv¨ rinen and E. Oja. a Independent component analysis: and applications. Neural Networks, 13(4-5):411–430, 2000. http://www.cs.helsinki.fi/u/ahyvarin/papers/NN00new.pdf. 1227 Algorithms URL A LMEIDA A. Hyv¨ rinen and P. Pajunen. a Nonlinear independent component analysis: tence and uniqueness results. Neural Networks, 12(3):429–439, 1999. http://www.cis.hut.fi/˜aapo/ps/NN99.ps. C. ExisURL Jutten and J. Karhunen. Advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixtures. International Journal of Neural Systems, 14(5):267–292, 2004. URL http://www.worldscinet.com/128/14/preserved-docs/1405/S012906570400208X.pdf. A. Kraskov, H. St¨ gbauer, and P. Grassberger. Estimating mutual information. Physical Review E, o 69:066138, 2004. URL http://arxiv.org/pdf/cond-mat/0305641. H. Lappalainen and A. Honkela. Bayesian nonlinear independent component analysis by multilayer perceptrons. In M. Girolami, editor, Advances in Independent Component Analysis, pages 93–121. Springer-Verlag, 2000. URL http://www.cis.hut.fi/harri/ch7.ps.gz. S.-I. Lee and S. Batzoglou. Application of independent component analysis to microarrays. Genome Biology, 4(11):R76, 2003. URL http://genomebiology.com/2003/4/11/R76. J. B. Maintz. A survey of medical image registration. Medical Image Analysis, 2(1):1–36, 1998. URL http://www.cs.uu.nl/people/twan/personal/media97.pdf. G. C. Marques and L. B. Almeida. Separation of nonlinear mixtures using pattern repulsion. In J. F. Cardoso, C. Jutten, and P. Loubaton, editors, Proceedings of the First International Workshop on Independent Component Analysis and Signal Separation, pages 277–282, Aussois, France, 1999. URL http://www.lx.it.pt/˜lbalmeida/papers/MarquesAlmeidaICA99.ps.zip. F. Palmieri, D. Mattera, and A. Budillon. Multi-layer independent component analysis (MLICA). In J. F. Cardoso, C. Jutten, and P. Loubaton, editors, Proceedings of the First International Workshop on Independent Component Analysis and Signal Separation, pages 93–97, Aussois, France, 1999. B. Pearlmutter and L. Parra. A context-sensitive generalization of independent component analysis. In International Conference on Neural Information Processing, Hong Kong, 1996. URL http://newton.bme.columbia.edu/˜lparra/publish/iconip96.pdf. J. Schmidhuber. Learning factorial codes by predictability minimization. Neural Computation, 4 (6):863–879, 1992. A. Taleb and C. Jutten. Source separation in post-nonlinear mixtures. IEEE Transactions on Signal Processing, 47:2807–2820, 1999. F. J. Theis, C. G. Puntonet, and E. W. Lang. Nonlinear geometric ICA. In Proceedings of the International Workshop on Independent Component Analysis and Blind Signal Separation, pages 275– 280, Nara, Japan, 2003. URL http://homepages.uni-regensburg.de/˜thf11669/public ations/theis03nonlineargeo_ICA03.pdf. 1228 S EPARATING A R EAL -L IFE N ONLINEAR I MAGE M IXTURE H. Valpola and J. Karhunen. An unsupervised ensemble learning method for nonlinear dynamic state-space models. Neural Computation, 14(11):2647–2692, 2002. URL http://www.cis.hut.fi/harri/papers/ValpolaNC02.pdf. 1229