Discovering Class Specific Composite Features Through Discriminative Sampling with Swendsen-Wang Cut

Abstract

This paper proposes a novel approach to discover a set of class specific composite features as the feature pool for the detection and classification of complex objects using AdaBoost. Each composite feature is constructed from the combination of multiple individual features. Unlike previous works that design features manually or with certain restrictions, the class specific features are selected from the space of all combinations of a set of individual features. To achieve this, we first establish an analogue between the problem of discriminative feature selection and generative image segmentation, and then draw discriminative samples from the combinatory space with a novel algorithm called discriminative generalized Swendsen-Wang cut. These samples form the initial pool of features, where AdaBoost is applied to learn a strong classifier combining the most discriminative composite features. We demonstrate the efficacy of our approach by comparing with existing detection algorithms for finding people in general pose.