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Proceedings Paper

Quantitative evaluation of superpixel clustering
Author(s): Dylan Stewart; Alina Zare; J. Tory Cobb
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Paper Abstract

Superpixel segmentation methods have been found to be increasingly valuable in image processing and analysis. Superpixel segmentation approaches have been used as a preprocessing step for a wide variety of image analysis tasks such as full scene segmentation, automated scene understanding, object detection and classification, and have been used to reduce computation time during these tasks. While many quantitative evaluation metrics have been developed in the literature to analyze traditional image segmentation and clustering results, these metrics have not been used or adapted to quantitatively evaluate superpixel segmentations. In this paper, multiple superpixel segmentation algorithms are applied to synthetic aperture sonar (SAS) imagery and the results are evaluated using cluster validity indices that have been adapted for superpixel segmentation. Both cluster validity metrics that rely only on internal measures as well as those that use both internal and external measures are considered. Results are shown on a synthetic aperture sonar (SAS) data set.

Paper Details

Date Published: 30 April 2018
PDF: 10 pages
Proc. SPIE 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 1062819 (30 April 2018); doi: 10.1117/12.2305518
Show Author Affiliations
Dylan Stewart, Univ. of Florida (United States)
Alina Zare, Univ. of Florida (United States)
J. Tory Cobb, Naval Surface Warfare Ctr. Panama City Div. (United States)


Published in SPIE Proceedings Vol. 10628:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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