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Published Articles >> Table of Contents >> Abstract
17th International Conference on Pattern Recognition (ICPR'04) - Volume 2
pp. 44-47
Grouping with Bias for Distribution-Free Mixture Model Estimation
Richard Nock, GRIMAAG/Univ. Antilles-Guyane, France
Vincent Page, GRIMAAG/Univ. Antilles-Guyane, France
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.1334031
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| Abstract |
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Some authors have recently devised adaptations of spectral grouping algorithms to integrate prior knowledge, as constrained eigenvalues problems. In this paper, we adapt recent statistical grouping algorithms to this task, as a non-parametric mixture model estimation problem. The approach appears to be attractive for its theoretical benefits, and its experimental results, as light bias brings dramatic improvements over unbiased approaches on hard images.
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Citation:
Richard Nock, Vincent Page,
"Grouping with Bias for Distribution-Free Mixture Model Estimation,"
icpr,
pp. 44-47,
17th International Conference on Pattern Recognition (ICPR'04) - Volume 2,
2004
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