ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Power Spectral Clustering

Challa, A and Danda, S and Sagar, BSD and Najman, L (2020) Power Spectral Clustering. In: Journal of Mathematical Imaging and Vision, 62 . pp. 1195-1213.

[img] PDF
jou_mat_ima_vis_62_1195–1213_2020.pdf - Published Version
Restricted to Registered users only

Download (4MB) | Request a copy
Official URL: https://doi.org/10.1007/s10851-020-00980-7


Spectral clustering is one of the most important image processing tools, especially for image segmentation. This specializes at taking local information such as edge weights and globalizing them. Due to its unsupervised nature, it is widely applicable. However, traditional spectral clustering is O(n3 / 2). This poses a challenge, especially given the recent trend of large datasets. In this article, we propose an algorithm by using ideas from �-convergence, which is an amalgamation of maximum spanning tree clustering and spectral clustering. This algorithm scales as O(nlog (n)) under certain conditions, while producing solutions which are similar to that of spectral clustering. Several toy examples are used to illustrate the similarities and differences. To validate the proposed algorithm, a recent state-of-the-art technique for segmentation�multiscale combinatorial grouping is used, where the normalized cut is replaced with the proposed algorithm and results are analyzed. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Item Type: Journal Article
Publication: Journal of Mathematical Imaging and Vision
Publisher: Springer Nature
Additional Information: Copyright for this article belongs to Springer Nature.
Keywords: Image segmentation; Large dataset; Metals; Trees (mathematics), Gamma convergence; Image processing tools; Large datasets; Local information; Maximum spanning tree; Normalized cuts; Power spectral; Spectral clustering, Clustering algorithms
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 11 Nov 2021 14:42
Last Modified: 11 Nov 2021 14:42
URI: http://eprints.iisc.ac.in/id/eprint/66113

Actions (login required)

View Item View Item