Learning by a generation approach to appearance-based object recognition

We propose a methodology for the generation of learning samples in appearance-based object recognition. In many practical situations, it is not easy to obtain a large number of learning samples. The proposed method learns object models from a large number of generated samples derived from a small number of actually observed images. The learning algorithm has two steps: 1) generation of a large number of images by image interpolation, or image deformation, and 2) compression of the large sample sets using parametric eigenspace representation. We compare our method with the previous methods that interpolate sample points in eigenspace, and show the performance of our method to be superior. Experiments were conducted for 432 image samples for 4 objects to demonstrate the effectiveness of the method.