Reducing the computation time of the isodata and K-means unsupervised classification algorithms

International Geoscience and Remote Sensing Symposium (IGARSS)

Volumn 3, Issue , 2002, Pages 1627-1629

  Phillips, Steven ^a  

^a AT AND T LABS RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; IMAGE ANALYSIS; IMAGE ENHANCEMENT; IMAGE SENSORS; ITERATIVE METHODS; MERGING; PATTERN RECOGNITION;

ASYMPTOTIC COMPLEXITY; ISODATA ALGORITHM; K-MEANS ALGORITHM;

REMOTE SENSING;

EID: 0036399517     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. D. Hall and G. Ball. Isodata: a novel method of data analysis and pattern classification. Technical report, Stanford Research Institute, 1965.
2. J. R. Jensen. Introductory Digital Image Processing, A Remote Sensing Perspective. Prentice Hall, Upper Saddle River, NJ, 1996.
3. J. MacQueen. Some methods for classification and analysis of multivariate observations. In Proc. Fifth Berkeley Symposium on Mathematics, Statistics and Probability, pages 281-296, 1967.
4. P. Montolio, A. Gasull, E. Monte, L. Torres, and F. Marqués. Analysis and optimization of the k-means algorithm for remote sensing applications. In N. de la Blanca, A. Sanfeliu, and E. Vidal, editors, Pattern Recognition and Image Analysis, pages 155-170. World Scientific, 1992.
5. A. W. Moore. The anchors hierarchy: Using the triangle inequality to survive high dimensional data. In Proc. UAI-2000: Uncertainty in Artificial Intelligence, 2000.
6. S. Phillips. Acceleration of K-means and related clustering algorithms. In Proc. ALENEX, 2002.