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Image-Driven Data Mining for Image Content Segmentation, Classification, and Attribution.
- Source :
-
IEEE Transactions on Geoscience & Remote Sensing . Sep2007, Vol. 45 Issue 9, p2964-2978. 15p. - Publication Year :
- 2007
-
Abstract
- Image-driven data mining methods are described for image content segmentation, classification, and attribution, where each pixel location of an image-under-analysis is the center point of a pixel-block query that returns an estimated class label. Feature attribute estimates may also be mined when sufficient attribute strata exist in the data warehouse. Novel methods are presented for pixel-block mining, pattern similarity scoring, class label assignments, and attribute mining. These methods are based on a direct sum tree structure called a σ-tree that is utilized with near-neighbor similarity scoring. The σ-tree structure provides a solution to the challenge of high computation/memory costs of pixel-block similarity searching. The σ-trees are integrated into warehouse subsystems that provide referential capability into feature attribute data, resulting in a foundation for data mining called Source Optimized, Labeled, DIgital Expanded Representations (SOLDIER). The variable depth "bit-plane" data representations produced by σ-tree path selections provide an approach to image content segmentation, and provide a structure for formulation of Bayesian classification with data-adaptive Parzen classifiers with variably sized windows. Preliminary methods and results for postprocessing of mined feature-thematic layers for higher level scene understanding are also presented. Sample results are shown with synthetic aperture radar images and with high-resolution pan-sharpened satellite images of the Payagala, Sri Lanka area before the site was devastated by the 2004 Asian Tsunami. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 45
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 26507923
- Full Text :
- https://doi.org/10.1109/TGRS.2007.898235