This paper applies a Bayesian classification scheme to the problem of object recognition through probabilistic modeling of local color histograms. In this context, the density estimation is generally performed via nonparametric kernel methods and the high dimensionality does not allow precision in the results. We propose a local independent component analysis (ICA) representation of the data. Within this representation, the components can be assumed statistically independent and, for this particular problem, sparsity of the independent components is observed. We show how these two characteristics simplify and add accuracy to the density estimation and develop a Bayesian decision scheme within this representation. We propose a set of possible density estimations for supergaussian densities, the density type associated with a sparse representation. Two experiments were performed. The first one illustrates the properties of the ICA representation for local color histograms. The second experiment tests the ICA classification model for a large set of pharmaceutical products and compares this scheme with a nonparametric technique based on Gaussian Kernels, two nearest-neighbor techniques and global histogram approach. © 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
- Bayesian classification
- Color histograms
- Density estimation
- Independent component analysis
- Statistical pattern recognition