Scalable image segmentation via decoupled sub-graph compression
| dc.contributor.author | Medeiros, Rafael Sachett | |
| dc.contributor.author | Wong, Alexander | |
| dc.contributor.author | Scharcanski, Jacob | |
| dc.date.accessioned | 2018-03-21T13:41:59Z | |
| dc.date.available | 2018-03-21T13:41:59Z | |
| dc.date.issued | 2018-06-01 | |
| dc.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/ | en |
| dc.description.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. | en |
| dc.identifier.uri | http://dx.doi.org/10.1016/j.patcog.2017.11.028 | |
| dc.identifier.uri | http://hdl.handle.net/10012/13051 | |
| dc.language.iso | en | en |
| dc.publisher | Elsevier | en |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | Decoupling | en |
| dc.subject | Graph compression | en |
| dc.subject | Scalability | en |
| dc.subject | Segmentation | en |
| dc.title | Scalable image segmentation via decoupled sub-graph compression | en |
| dc.type | Article | en |
| dcterms.bibliographicCitation | Medeiros, R. S., Wong, A., & Scharcanski, J. (2018). Scalable image segmentation via decoupled sub-graph compression. Pattern Recognition, 78, 228–241. https://doi.org/10.1016/j.patcog.2017.11.028 | en |
| uws.contributor.affiliation1 | Faculty of Engineering | en |
| uws.contributor.affiliation2 | Systems Design Engineering | en |
| uws.peerReviewStatus | Reviewed | en |
| uws.scholarLevel | Faculty | en |
| uws.typeOfResource | Text | en |
| uws.typeOfResource | Text | en |