Incremental clustering and dynamic information retrieval
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
A constant-factor approximation algorithm for the k-median problem (extended abstract)
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Spectral partitioning with multiple eigenvectors
Discrete Applied Mathematics - Special volume on VLSI
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Sublinear Time Approximation Scheme for Clustering in Metric Spaces
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
On clusterings-good, bad and spectral
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Graph Partition by Swendsen-Wang Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
A linear-time approach for image segmentation using graph-cut measures
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
A simple heuristic for the p-centre problem
Operations Research Letters
Hi-index | 0.98 |
Spectral clustering algorithms have attracted considerable attention in recent years. However, a problem still exists. These approaches are too slow to scale to large problem sizes. This paper aims at addressing a coarsening algorithm for efficiently grouping large-dataset objects within multi-band images. The coarsening algorithm is based on random graph theory, and it proceeds by combining local homogeneous resolution cells into a set of irregular blocks so the spectral clustering algorithms run efficiently at some coarse level. For multi-band images, we formulate the similarity between pairwise objects as a novel normalized expression and reformulate it in the form of a matrix so that we can implement our algorithm in a few lines using IDL. Finally, we illustrate two examples in agriculture which confirm the effectiveness and efficiency of the proposed algorithm.