Minimum Bayes Error Features for Visual Recognition by Sequential Feature Selection and Extraction

The extraction of optimal features, in a classification sense, is still quite challenging in the context of large-scale classification problems (such as visual recognition), involving a large number of classes and significant amounts of training data per class. We present an optimal, in the minimum Bayes error sense, algorithm for feature design that combines the most appealing properties of the two strategies that are currently dominant: feature extraction (FE) and feature selection (FS). The new algorithm proceeds by interleaving pairs of FS and FE steps, which amount to a sequential search for the most discriminant directions in a collection of two dimensional subspaces. It combines the fast convergence rate of FS with the ability of FE to uncover optimal features that are not part of the original basis functions, leading to solutions that are better than those achievable by either FE or FS alone, in a small number of iterations. Because the basic iteration has very low complexi...

• Type: Conference
• Year: 2005
• Where: CRV
• Authors: Gustavo Carneiro, Nuno Vasconcelos
• Keywords: Bayes Error Sense, CRV 2005, Feature Extraction, Optimal Features, Robotics