1. Image-Driven Data Mining for Image Content Segmentation, Classification, and Attribution.
- Author
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Barnes, Christopher F.
- Subjects
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DATA mining , *IMAGE processing , *INFORMATION organization , *PIXELS , *IMAGE analysis , *IMAGING systems , *BAYESIAN analysis , *INFORMATION storage & retrieval systems , *INFORMATION processing , *DATABASE management - 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]
- Published
- 2007
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