Local Feature Matching Using Entropic Graphs

We present a general framework for image discrimination based on identifying small, localized differences between images. Our novel matching scheme is based on an alternate information divergence criterion, the R?enyi ? -entropy. The minimum spanning tree (MST) is used to derive a direct estimate of ? -entropy over a feature set defined by basis features extracted from images using independent componenet analysis (ICA). The MST provides a stable unbiased estimate of local entropy to identify sites of local mismatch between images. Sub-image blocks are ranked over a set of local deformations spanning small image regions. We demonstrate improved sensitivity to local changes for matching and registration and provide a framework for tracking features of interest in images.

• Type: Conference
• Year: 2004
• Where: ISBI
• Authors: Huzefa Neemuchwala, Alfred O. Hero, Paul L. Carson, Charles R. Meyer
• Keywords: ISBI 2004, Local Mismatch, Medical Imaging, Minimum Spanning Tree, Stable Unbiased Estimate