title: MICA: a multilinear ICA decomposition for natural scene modeling.
abstract: We refine the classical independent component analysis (ICA) decomposition using a multilinear expansion of the probability density function of the source statistics. In particular, we introduce a specific nonlinear system that allows us to elegantly capture the statistical dependences between the responses of the multilinear ICA (MICA) filters. The resulting multilinear probability density is analytically tractable and does not require Monte Carlo simulations to estimate the model parameters. We demonstrate the MICA model on natural image textures and envision that the new model will prove useful for analyzing nonstationarity natural images using natural scene statistics models.
authors.affiliation: Center for Perceptual Systems, Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX 78712, USA. rraj@ece.utexas.edu
authors.forename: Raghu G
authors.initials: RG
authors.lastname: Raj
authors.forename: Alan C
authors.initials: AC
authors.lastname: Bovik
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis