A discriminative model for object representation and detection via sparse features

Abstract

This paper proposes a discriminative model that represents an object category with a batch of boosted image patches, motivated by detecting and localizing objects with sparse features. Instead of designing features carefully and category-specifically as in previous work, we extract a massive number of local image patches from the positive object instances and quantize them as weak classifiers. Then we extend the Adaboost algorithm for learning the patch-based model integrating object appearance and structure information. With the learned model, a few features are activated to localize instances in the testing images. In the experiments, we apply the proposed method with several public datasets and achieve advancing performance. © 2010 IEEE.