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Fast Feature Extraction Approach for Multi-Dimension Feature Space Problems

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概要 Recently, we proposed a fast feature extraction approach denoted FSOM utilizes Self Organizing Map (SOM). FSOM [1] overcomes the slowness of traditional SOM search algorithm. We investigated the super...iority of the new approach using two lip reading data sets which require a limited feature space as the experiments in [1] showed. In this paper, we continue FSOM investigation but using an RGB face recognition database across different poses and different lighting conditions. We believe that such data sets require multi-dimensional feature space to extract the information included in the original data in an effective way especially if you have a big number of classes. Again, we show here how is FSOM reduces the feature extraction time of traditional SOM drastically while preserving same SOM’s qualities.続きを見る

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登録日 2009.04.22
更新日 2017.02.28