Learning Shape Detector by Quantizing Curve Segments with Multiple Distance Metrics

In this paper, we propose a very efficient method to learn shape models using local curve segments with multiple types of distance metrics. Our learning approach includes two key steps: feature generation and model pursuit. In the first step, for each category, we first extract a massive number of local “prototype” curve segments from a few roughly aligned shape instances. Then we quantize these curve segments with three types of distance metrics corresponding to different shape deformations. In each metric space, the quantized curve segments are further grown (spanned) into a large number of ball-like manifolds, and each of them represents a equivalence class of shape variance. In the second step of shape model pursuit, using these manifolds as features, we propose a fast greedy learning algorithm based on the information projection principle. The algorithm is guided by a generative model, and stepwise selects the features that have maximum information gain. The advantage of the proposed method is identified on several public datasets and summarized as follows. (1) Our models consisting of local curve segments with multiple distance metrics are robust to the various shape deformations, and thus enable us to perform robust shape classification and detect shapes against background clutter. (2) The auto-generated curve-based features are very general and convenient, rather than designing specific features for each category.