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Title : OBJECT-SPECIFIC FEATURE EXTRACTION VIA MARKOV RANDOM FIELDS DERIVED FROM 0TH-ORDER SIGMA-TREE SEGMENTATIONS
Company : Georgia Institute of Technology
File Name : AliKhan.pdf
Size : 467795
Type : application/pdf
Date : 08-May-2010
Downloads : 6

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Featured Paper by

Syed Irteza Ali Khan and Christopher F. Barnes

Sigma-Trees associated with residual vector quantization (RVQ) has been used for image-driven data mining to detect features and objects in a digital image with a degree of success. RVQ methods based on σ-tree structures have been designed to implement successive refinement of information for image segmentation. In such implementations, RVQ based novel methods are devised for pixel-block mining, pattern similarity scoring, class label assignments and attribute mining (Barnes, 2007a). Direct sum σ-tree structures are used for near-neighbor similarity scoring. The variable bit-plane data representations produced by σ-tree structures not only provides an approach for image content segmentation and a structure for formulation of Bayesian classification, but also offers a solution to the challenge of high computational costs involved in pixel-block similarity searching.
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