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Learning Non-Metric Partial Similarity Based on Maximal Margin Criterion

9 years 5 months ago
Learning Non-Metric Partial Similarity Based on Maximal Margin Criterion
The performance of many computer vision and machine learning algorithms critically depends on the quality of the similarity measure defined over the feature space. Previous works usually utilize metric distances which are often epistemologically different from the perceptual distance of human beings. In this paper, a novel non-metric partial similarity measure is introduced, which is born to automatically capture the prominent partial similarity between two images while ignoring the confusing unimportant dissimilarity. This measure is potentially useful in face recognition since it can help identify the inherent intra-personal similarity and thus reducing the influence caused by large variations such as expression and occlusions. Moreover, to make this method practical, this paper proposes an automatic and class-dependent similarity threshold setting mechanism based on the maximal margin criterion, and uses a SelfOrganization Map-based embedding technique to alleviate the computatio...
Xiaoyang Tan, Songcan Chen, Jun Li, Zhi-Hua Zhou
Added 10 Jun 2010
Updated 10 Jun 2010
Type Conference
Year 2006
Where CVPR
Authors Xiaoyang Tan, Songcan Chen, Jun Li, Zhi-Hua Zhou
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