Multi-Scale Shape Boltzmann Machine: A Shape Model Based on Deep Learning Method

Abstract

Shape modelling is very important in many tasks of computer vision in the internet of things. Shape Boltzmann Machine (SBM) is a strong shape model, having ability to capture the details of object shape by introducing the Local Receptive Fields (LRF) and weight sharing into a deep learning architecture. However, applying LRF only in a single layer restrict its capabilities of learning more de-tails of object shape and representation of local shape parts. In this paper, we propose a new shape model based on Deep Boltzmann Machine (DBM) which we call Multi-Scale Shape Boltzmann Machine (MSSBM). By introducing weight sharing and LRF hierarchically in a deep architecture, MSSBM is capable of learning the true binary distributions of training shapes and generating more realistic shapes than the existing models, such as Deep Belief Network (DBN), DBM, SBM. Such capabilities make MSSBM suitable for many vision tasks, for example, image segmentation, object detection and inpainting, by enforcing shape prior knowledge. We demonstrate the performance of MSSBM through several experiments on three different datasets, in which exploitation of the details of shape structure is important for capturing the statistical variability of the underlying shape distributions. Experimental results show that MSSBM is a strong model for representing binary shapes that contains complex structure features.