Multiple view based 3D object classification using ensemble learning of local subspaces

Multiple observation improves the performance of 3D object classification. However, since the distribution of feature vectors obtained from multiple view points have strong nonlinear structure, the kernel-based methods are often introduced with nonlinear mapping. By mapping feature vectors to a higher dimensional space, kernel-based methods transform the distribution to weaken its nonlinearity. Although they have been succeeded in many applications, their computation cost is large. Therefore we aim to construct a comparable method with the kernel-based methods without using nonlinear mapping. Firstly we attempt to approximate a distribution of feature vectors with multiple local subspaces. Secondly we combine local subspace approximation with ensemble learning algorithm to form a new classifier. We will demonstrate that our method can achieve comparable performance with kernel methods through evaluation experiments using multiple view images of 3D objects from a public data set.