Relative Margin Support Tensor Machines for gait and action recognition
Kotsia, Irene and Patras, Ioannis (2010) Relative Margin Support Tensor Machines for gait and action recognition. In: ACM International Conference on Image and Video Retrieval (CIVR 10), 5 - 7 July 2010, Xidian, China.
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In this paper, we formulate the Relative Margin Support Tensor Machines (RMSTMs) problem as an extension of the Relative Margin Machines (RMMs). While the typical Support Tensor Machines (STMs) find a solution that is greatly influenced by the data spread, the proposed RMSTMs maximize the margin in a way relative to the spread of the data. The difference in the obtained solutions can be significant in the cases of badly scaled data, especially in the case of varoius spreads across different data dimensions. The efficiency of the proposed method is illustrated on the problems of gait and action recognition, where the results acquired verify the superiority of the method in terms of classification performance.
|Item Type:||Conference or Workshop Item (Paper)|
|Research Areas:||A. > School of Science and Technology > Computer Science
A. > School of Science and Technology > Computer Science > Intelligent Environments Research Group
|Depositing User:||Devika Mohan|
|Date Deposited:||03 Dec 2012 06:06|
|Last Modified:||13 Oct 2016 14:25|
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