Exploiting data parallelism in the yConvex hypergraph algorithm for image representation using GPGPUs

TLDR: A parallel approach to implement yCHG model is proposed by exploiting massively parallel cores of NVIDIA Compute Unified Device Architecture (CUDA) by exploiting the high level data parallelism available on Graphic Processing Units (GPUs).

Abstract: To define and identify a region-of-interest (ROI) in a digital image, the shape descriptor of the ROI has to be described in terms of its boundary characteristics. To address the generic issues of contour tracking, the yConvex Hypergraph (yCHG) model was proposed by Kanna et al [1]. This yCHG model represents any connected region as a finite set of disjoint yConvex hyperedges (yCHE), which helps to perform the contour tracking precisely without retracing the same contour. We observe that the serial implementation of the yCHG is quite costly in terms of memory and computation for high resolution images. These issues motivated us to exploit the high level data parallelism available on Graphic Processing Units (GPUs). In this work, we propose a parallel approach to implement yCHG model by exploiting massively parallel cores of NVIDIA Compute Unified Device Architecture (CUDA). We perform our experiments on the MODIS satellite image database by NASA, and based on our analysis we observe that the performance of the serial implementation is better on smaller images, but once the threshold is achieved in terms of image resolution, the parallel implementation outperforms its sequential counterpart by 2 to 10 times (2x-10x). We also conclude that an increase in the number of hyperedges in ROI of given size does not impact the performance of the overall algorithm.