Quantitative evaluation of superpixel clustering

Dylan Stewart; Alina Zare; J. Tory Cobb

doi:10.1117/12.2305518

30 April 2018 Quantitative evaluation of superpixel clustering

Dylan Stewart, Alina Zare, J. Tory Cobb

Proceedings Volume 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII; 1062819 (2018) https://doi.org/10.1117/12.2305518
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

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.

Citation Download Citation

Dylan Stewart, Alina Zare, and J. Tory Cobb "Quantitative evaluation of superpixel clustering", Proc. SPIE 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 1062819 (30 April 2018); https://doi.org/10.1117/12.2305518

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