Data augmentation using part analysis for shape classification

Abstract

Deep Convolutional Neural Networks have shown drastic improvements in the performance of various Computer Vision tasks. However, shape classification is a problem that has not seen state-of-the-art results using CNNs. The problem is due to the lack of large amounts of data to learn to handle multiple variations such as noise, pose variations, part articulations and affine deformations present in the shapes. In this paper, we introduce a new technique for augmenting 2D shape data that uses part articulations. This utilizes a novel articulation cut detection method to determine putative shape parts. Standard off-the-shelf CNN models trained with our novel data augmentation technique on standard 2D shape datasets yielded significant improvements over the state-of-the-art in most experiments and our data augmentation approach has the potential to be extended to other problems such as Image Classification and Object Detection.