Disjunctive Normal Shape Boltzmann Machine

Abstract

Shape Boltzmann machine (a type of Deep Boltzmann machine) is a powerful tool for shape modelling; however, has some drawbacks in representation of local shape parts. Disjunctive Normal Shape Model (DNSM) is a strong shape model that can effectively represent local parts of objects. In this paper, we propose a new shape model based on Shape Boltzmann Machine and Disjunctive Normal Shape Model which we call Disjunctive Normal Shape Boltzmann Machine (DNSBM). DNSBM learns binary distributions of shapes by taking both local and global shape constraints into account using a type of Deep Boltzmann Machine. The samples generated using DNSBM look realistic. Moreover, DNSBM is capable of generating novel samples that differ from training examples by exploiting the local shape representation capability of DNSM. We demonstrate the performance of DNSBM for shape completion on two different data sets in which exploitation of local shape parts is important for capturing the statistical variability of the underlying shape distributions. Experimental results show that DNSBM is a strong model for representing shapes that are composed of local parts.