Corner-Based Feature for Object Recognition

Feature extraction plays a very important role in object recognition and categorization. In this paper, we present a method for extracting corner-based feature from images. This feature is invariant to image scale and rotation, and is shown robust to addition of noise and changes in 3D viewpoint. This paper also describes an approach to using the feature for object recognition. As baselines for comparison, we implemented three additional recognition systems using signature, moment invariant and Fourier descriptor as features. They provide a good basis for judging the importance of representation in learning. The performance analysis on the obtained experimental results demonstrates that the proposed method is effective and efficient.