Learning Non-Metric Partial Similarity Based on Maximal Margin Criterion

Authors:
Xiaoyang Tan;Songcan Chen;Jun Li;Zhi-Hua Zhou
Affiliations:
Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China;Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China;Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China;Nanjing University, Nanjing 210093, China
Venue:
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Year:
2006

Citing 0
Cited 5

An adaptive image Euclidean distance

Pattern Recognition
Multi-instance clustering with applications to multi-instance prediction

Applied Intelligence
Face recognition under occlusions and variant expressions with partial similarity

IEEE Transactions on Information Forensics and Security
Non-metric label propagation

IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Similarity estimation using Bayes ensembles

SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 ofen 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 Self- Organization Map-based embedding technique to alleviate the computational problem. Experimental results show the feasibility and effectiveness of the proposed method.