Classifying visual objects with the consistency-driven pairwise comparisons method

Authors:
Waldemar W. Koczkodaj;Nicolas Robidoux;Ryszard Tadeusiewicz
Affiliations:
Laurentian University, Department of Mathematics and Computer Science, Sudbury, ON;Laurentian University, Department of Mathematics and Computer Science, Sudbury, ON;Computer Science, AGH University of Science and Technology
Venue:
Machine Graphics & Vision International Journal
Year:
2009

Citing 2
Cited 2

A Monte Carlo study of pairwise comparison

Information Processing Letters
On Saaty's and Koczkodaj's inconsistencies of pairwise comparison matrices

Journal of Global Optimization

Medical knowledge mining from image data: synthesis of medical image assessments for early stroke detection

Machine Graphics & Vision International Journal - Special issue on Image Databases
Multimedia information retrieval based on pairwise comparison and its application to visual search

Multimedia Tools and Applications

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Abstract

The classification of the various image features or visual objects can be carried out by the consistency-driven pairwise comparisons method based on their relative importance. A key issue in the proposed approach is a weight-based synthesis for combining various image features. When compared with the traditional experience-based linear assignment method, the proposed approach is more effective and easy to communicate.