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A Classification Based Similarity Metric for 3D Image Retrieval
Y. Liu and F. Dellaert
CVPR'98, June, 1998, pp. 800 - 805.

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We present a principled method of obtaining a weighted similarity metric for 3D image retrieval, firmly rooted in Bayes decision theory. The basic idea is to determine a set of most discriminative features by evaluating how well they perform on the task of classifying images according to predefined semantic categories. We propose this indirect method as a rigorous way to solve the difficult feature selection problem that comes up in most content based image retrieval tasks. The method is applied to normal and pathological neuroradiological CT images, where we take advantage of the fact that normal human brains present an approximate bilateral symmetry which is often absent in pathological brains. The quantitative evaluation of the retrieval system shows promising results. the semantics of an image. This domain also provides

Associated centers: VASC and MRTC
Associated labs/groups: Biomedical Image Analysis, Computational Symmetry, and Medical Robotics and Computer Assisted Surgery
Associated project: A Statistical Quantification of Human Brain Asymmetry

Number of pages: 6

Y. Liu and F. Dellaert, "A Classification Based Similarity Metric for 3D Image Retrieval," CVPR'98, June, 1998, pp. 800 - 805.

@inproceedings{Liu_1998_574,
   author = "Yanxi Liu and Frank Dellaert",
   title = "A Classification Based Similarity Metric for 3D Image Retrieval",
   booktitle = "CVPR'98",
   month = "June",
   year = "1998",
   pages = "800 - 805"
}