In this paper, a real-time application for visual inspection and classification of cork stoppers is presented. The process of cork inspection and quality grading is based on analyzing a large set of characteristics corresponding to visual features that are related to cork porosity. We have applied a set of nonparametric and parametric classification methods for comparing and evaluating their performance in this real problem. The best results have been achieved using Bayesian classification through probabilistic modeling in a high-dimensional space. In this context, it is well known that high dimensionality represents a serious problem for density estimation. We propose a class-conditional independent component analysis representation of the data that allows an accurate estimation of the data probability density function by factorizing it. The method has achieved a success of 98% of correct classification

A new modeling technique, based on independent component analysis (ICA), is proposed to represent and recognize high-dimensional samples from a large set of classes. The model is constructed via density estimation techniques, and recognition is performed in the Bayesian decision framework. We show that the technique can be successfully used for automatic object identification in environments where a visual observer is faced with a classification problem in high-dimensional spaces with a large number of classes. A first experiment illustrates that classification using an ICA representation is a technique that, even in low dimensions, performs comparably to standard classification techniques. The second experiment tests the ICA classification model on high-dimensional data. Recognition was performed using local color histograms of images corresponding to 400 different objects. It is also shown how our approach outperforms other techniques commonly used in the context of appearance-based recognition.

This paper is focused on the problems of feature selection and classification when classes are modeled by statistically independent features. We show that, under the assumption of class-conditional independence, the class separability measure of divergence is greatly simplified, becoming a sum of unidimensional divergences, providing a feature selection criterion where no exhaustive search is required. Since the hypothesis of independence is infrequently met in practice, we also provide a framework making use of class-conditional Independent Component Analyzers where this assumption can be held on stronger grounds. Divergence and the Bayes decision scheme are adapted to this class-conditional representation. An algorithm that integrates the proposed representation, feature selection technique, and classifier is presented. Experiments on artificial, benchmark, and real-world data illustrate our technique and evaluate its performance.

This paper applies a Bayesian classification scheme to the problem of recognition through probabilistic modeling of high dimensional data. In this context, high dimensionality does not allow precision in the density estimation. We propose a local independent component analysis (ICA) representation of the data. The components can be assumed statistically independent and, in many cases, sparsity is observed. We show how these two characteristics can be used to simplify and add accuracy to the density estimation and develop bayesian decision within this representation. A first experiment illustrates that classification using an ICA representation is a technique that, even in low dimensions, performs comparably to standard classification techniques. The second experiment tests the ICA classification model on high dimensional data. Recognition was performed using local color histograms as salient features. It is also shown how our approach outperforms other techniques commonly used in the context of appearance-based recognition.

The paper presents an improvement of the classical Non-negative Matrix Factorization (NMF) approach for dealing with local representations of image objects. NHF, when applied to global data representations such as faces presents a high ability to represent local features of the original data in an unsupervised way. However, when applied to local representations, NMF generates redundant basis. This work implements an improvement on the original NMF approach by incorporating prior knowledge in the form of a weight matrix extracted from the training data. A detailed mathematical description of the inclusion of this weight matrix is provided, and results demonstrating its advantages are included. Furthermore, the original NMF approach lacks a hierarchy of the elements of the estimated basis. A technique to determine an ordered set of discriminant basis is also presented. Finally, the effectiveness of the weighted approach with respect to the classical approach is experimentally compared. This is done by implementing a clustering algorithm that automatically extracts object parts from the NMF representation of an image database corresponding to newspapers.