A Classification Based Similarity Metric for 3D Image Retrieval

Yanxi Liu and Frank Dellaert
CVPR'98, July, 1998, pp. 800 - 805.


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Abstract
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

Keywords
bilateral symmetry, machine learning, image indexing, image semantics

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Medical Robotics Technology Center
Associated Lab(s) / Group(s): Medical Robotics and Computer Assisted Surgery, Computational Symmetry, Biomedical Image Analysis
Associated Project(s): A Statistical Quantification of Human Brain Asymmetry
Number of pages: 6

Text Reference
Yanxi Liu and Frank Dellaert, "A Classification Based Similarity Metric for 3D Image Retrieval," CVPR'98, July, 1998, pp. 800 - 805.

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