Classification based similarity metric for 3D image retrieval

Yanxi Liu, Frank Dellaert

Research output: Contribution to journalArticle

29 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)800-805
Number of pages6
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - 1998

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Image retrieval
Brain
Decision theory
Feature extraction
Semantics

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Software
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

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