Recognizing 3-D Objects with Linear Support Vector Machines
In this paper we propose a method for 3-D object recognition based on linear Support Vector Machines (SVMs). Intuitively, given a set of points which belong to either of two classes, a linear SVM finds the hyperplane leaving the largest possible fraction of points of the same class on the same side, while maximizing the distance of either class from the hyperplane. The hyperplane is determined by a subset of the points of the two classes, named support vectors, and has a number of interesting theoretical properties. The proposed method does not require feature extraction and performs recognition on images regarded as points of a space of high dimension. We illustrate the potential of the recognition system on a database of 7200 images of 100 different objects. The remarkable recognition rates achieved in all the performed experiments indicate that SVMs are well-suited for aspect-based recognition, even in the presence of small amount of occlusions.