A Hierarchical Object Recognition System Based on Multi-scale Principal Curvature Regions

This paper proposes a new generic object recognition system based on multi-scale affine-invariant image regions. Image segments are obtained by a watershed transform of the principal curvature of a contrast enhanced image. Each region is described by an intensity-based statistical descriptor and a PCA-SIFT descriptor. The spatial relations between regions are represented by a cluster-index distribution histogram. With these new descriptors, we develop a hierarchical object recognition system which uses an improved boosting feature selection method (Opelt et al., 2004) to construct layer classifiers by automatically selecting the most discriminative features in each layer. All layer classifiers are then combined to give the final classification. This system is tested on various object recognition problems. Experimental results show that the new hierarchical system outperforms the comparable solutions on most of the datasets tested