Support vector machines for 3D object recognition

TLDR

Support vector machines (SVMs) are a recent pattern‑recognition technique that selects a hyperplane maximizing the margin between two classes, using support vectors and possessing several theoretical advantages. The study applies linear SVMs to 3D object recognition. The authors evaluate linear SVMs on 7,200 images of 100 objects, treating each image as a high‑dimensional point without feature extraction or pose estimation. The experiments achieved excellent recognition rates, demonstrating that SVMs are well‑suited for aspect‑based recognition.

Abstract

Support vector machines (SVMs) have been recently proposed as a new technique for pattern recognition. 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. In this paper, we use linear SVMs for 3D object recognition. We illustrate the potential of SVMs on a database of 7200 images of 100 different objects. The proposed system does not require feature extraction and performs recognition on images regarded as points of a space of high dimension without estimating pose. The excellent recognition rates achieved in all the performed experiments indicate that SVMs are well-suited for aspect-based recognition.