Parallelization of a Hierarchical Graph-Based Image Segmentation using OpenMP

Year 2016, Special Issue (2016), 6 - 11, 01.12.2016

Ali Sağlam , Nurdan Akhan Baykan

https://doi.org/10.18100/ijamec.271038

Abstract

In many image-processing applications, image segmentation is an
essential stage. In this stage, an image is partitioned into several regions
according to the similarity of its pixels. In addition to the accuracy of the
image segmentation, the speed is also very important for real-time image
processing applications. Many computer applications take advantages of the
multi-processor architecture to up to their running performance. However, to
run an algorithm as parallel is very difficult in many cases. Due to using the
same memory blocks, many conflicts might be happened between the processors.
Moreover, each process of one processor may depend on those of another
processor. For this reason, the algorithm to be parallelized must be suitable
to parallel. In addition, the processing traffic that is pursued by the
processors must be controlled within some parallel directives. In this paper,
we provide a parallel implementation to a hierarchical graph-based image
segmentation method by using its hierarchical processing steps. To achieve this
goal, we utilize the OpenMP (Open Multi-Processing) Library to run the
segmentation process as parallel on images of different sizes from the INRIA
Holidays dataset. The experimental results show that the parallel
implementation of the algorithm is more effective than the serial type
according to processing time.

Keywords

Parallel programming, Image segmentation, Graph, OpenMP

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