Object recognition based on deformable edge set

We aim to solve the object recognition problem by a novel contour feature called Deformable Edge Set (DES). The DES consists of several Deformable Edge Features (DEF), which is deformed from an edge template to the actual object contour according to the distribution model of pixels. Then the DES is constructed based on the combination of DEF, where the arrangement and the deformable parameters are learned in a subspace. The RealAdaBoost algorithm is further utilized to select meaningful DES to localize the object. Experimental results show that the proposed approach not only locates the object bounding boxes but also captures the object contours well. It also achieves performance competitive with the commonly-used algorithms.