On the behavior of kernel mutual subspace method

Authors:
Hitoshi Sakano;Osamu Yamaguchi;Tomokazu Kawahara;Seiji Hotta
Affiliations:
NTT Communication Science Lab., "Keihanna Science City", Kyoto, Japan;Power and Industrial System R&D Center, Toshiba Corporation Power Systems Company, Fuchu-Shi, Tokyo, Japan;Corporate R&D Center, Toshiba Corporation, Kawasaki, Japan;The Graduate School of Engineering, Tokyo University of Agriculture and Technology, Koganei-shi, Tokyo, Japan
Venue:
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Year:
2010

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Abstract

Optimizing the parameters of kernel methods is an unsolved problem. We report an experimental evaluation and a consideration of the parameter dependences of kernel mutual subspace method (KMS). The following KMS parameters are considered: Gaussian kernel parameters, the dimensionalities of dictionary and input subspaces, and the number of canonical angles. We evaluate the recognition accuracies of KMS through experiments performed using the ETH- 80 animal database. By searching exhaustively for optimal parameters, we obtain 100% recognition accuracy, and some experimental results suggest relationships between the dimensionality of subspaces and the degrees of freedom for the motion of objects. Such results imply that KMS achieves a high recognition rate for object recognition with optimized parameters.