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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