Publication 2006 Issue No. 3 - March Abstract - Isoperimetric Graph Partitioning for Image Segmentation
Isoperimetric Graph Partitioning for Image Segmentation
March 2006 (vol. 28 no. 3)
pp. 469-475
 ASCII Text x Leo Grady, Eric L. Schwartz, "Isoperimetric Graph Partitioning for Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 469-475, March, 2006.
 BibTex x @article{ 10.1109/TPAMI.2006.57,author = {Leo Grady and Eric L. Schwartz},title = {Isoperimetric Graph Partitioning for Image Segmentation},journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},volume = {28},number = {3},issn = {0162-8828},year = {2006},pages = {469-475},doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.57},publisher = {IEEE Computer Society},address = {Los Alamitos, CA, USA},}
 RefWorks Procite/RefMan/Endnote x TY - JOURJO - IEEE Transactions on Pattern Analysis and Machine IntelligenceTI - Isoperimetric Graph Partitioning for Image SegmentationIS - 3SN - 0162-8828SP469EP475EPD - 469-475A1 - Leo Grady, A1 - Eric L. Schwartz, PY - 2006KW - Index Terms- Graph-theoretic methodsKW - graphs and networksKW - graph algorithmsKW - image representationKW - special architecturesKW - algorithmsKW - computer visionKW - applications.VL - 28JA - IEEE Transactions on Pattern Analysis and Machine IntelligenceER -
Spectral graph partitioning provides a powerful approach to image segmentation. We introduce an alternate idea that finds partitions with a small isoperimetric constant, requiring solution to a linear system rather than an eigenvector problem. This approach produces the high quality segmentations of spectral methods, but with improved speed and stability.

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Index Terms:
Index Terms- Graph-theoretic methods, graphs and networks, graph algorithms, image representation, special architectures, algorithms, computer vision, applications.
Citation:
Leo Grady, Eric L. Schwartz, "Isoperimetric Graph Partitioning for Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 469-475, March 2006, doi:10.1109/TPAMI.2006.57