Strangeness Based Feature Selection for Part Based Recognition

Motivated by recent approaches to object recognition, where objects are represented in terms of parts, we propose a new algorithm for selecting discriminative features based on strangeness measure. We will show that k-nearest neighbour strangeness can be used to measure the uncertainty of individual features with respect to the class labels and forms piecewise constant decision boundary. We study its properties and generalization capability by comparing it with optimal decision boundary and boundary obtained by k-nearest-neighbor methods. The proposed feature selection algorithm is tested both in simulation and real experiments, demonstrating that meaningful discriminative local features are selected despite the presence of large numbers of distractors. In the second stage we demonstrate how to integrate the local evidence provided by the selected features in the boosting framework in order to obtain the final strong classifier. The performance of the feature selection algorithm and the classifier is evaluated on the Caltech five object category database, achieving superior results in comparison with existing approaches at lower computational cost.