A novel object representation model for object detection

In this paper, we present a non-symmetry and anti-packing object pattern representation model (NAM). The NAM model codes the geometry of generic object categories as a hierarchy of sub-patterns and each sub-pattern is represented by a rich set of image cues. The sub-pattern-based NAM model is designed to decouple variations due to affine warps and other forms of shape deformations. The combination of multi-feature is to deal with the local variation of object. We then train part classifiers. Based on this model, we apply a generalized Hough voting scheme to generate object locations and scales. The experimental results on a variety of categories demonstrate that our method provides successful detection of the object within the image.