Variable selection for classification is a crucial paradigm in image analysis. Indeed, images are generally described by a large amount of features (pixels, edges …) although it is difficult to obtain a sufficiently large number of samples to draw reliable inference for classifications using the whole number of features. The authors describe in this chapter some simple and effective features selection methods based on filter strategy. They also provide some more sophisticated methods based on margin criterion or stochastic approximation techniques that achieve great performances of classification with a very small proportion of variables. Most of these “wrapper” methods are dedicated to a special case of classifier, except the Optimal features Weighting algorithm (denoted OFW in the sequel) which is a meta-algorithm and works with any classifier. A large part of this chapter will be dedicated to the description of the description of OFW and hybrid OFW algorithms. The authors illustrate also several other methods on practical examples of face detection problems.
A hybrid Feature Selection Optimization Model for High Dimension Data Classification
