Dynamic similarity kernel for visual recognition

Authors:
Wang Yan;Qingshan Liu;Hanqing Lu;Songde Ma
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Year:
2006

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Abstract

Inspired by studies of cognitive psychology, we proposed a new dynamic similarity kernel for visual recognition. This kernel has great consistency with human visual similarity judgement by incorporating the perceptual distance function. Moreover, this kernel can be seen as an extension of Gaussian kernel, and therefore can deal with nonlinear variations well like the traditional kernels. Experimental results on natural image classification and face recognition show its superior performance compared to other kernels.