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...

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Xiaoyang Tan, Songcan Chen, Jun Li, Zhi-Hua Zhou
• Keywords: Computer Vision, CVPR 2006, Partial Similarity, Partial Similarity Measure, Similarity Measure