Scalable image segmentation via decoupled sub-graph compression
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Date
2018-06-01
Authors
Medeiros, Rafael Sachett
Wong, Alexander
Scharcanski, Jacob
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Dealing with large images is an on-going challenge in image segmentation, where many of the current methods run into computational and/or memory complexity issues. This work presents a novel decoupled sub-graph compression (DSC) approach for efficient and scalable image segmentation. In DSC, the image is modeled as a region graph, which is then decoupled into small sub-graphs. The sub-graphs undergo a compression process, which simplifies the graph, reducing the number of vertices and edges, while keeping the overall graph structure. Finally, the compressed sub-graphs are re-coupled and re-compressed to form a final compressed graph representing the final image segmentation. Experimental results based on a dataset of high resolution images (1000 × 1500) show that the DSC method achieves better segmentation performance when compared to state-of-the-art segmentation methods (PRI=0.84 and F=0.61), while having significantly lower computational and memory complexity.
Description
The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.patcog.2017.11.028 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords
Decoupling, Graph compression, Scalability, Segmentation