Image Recognition Using Discrete Cosine Transforms as Dimensionality Reduction
نویسندگان
چکیده
Principal Component Analysis (PCA) approaches to image recognition are data dependent and computationally expensive. To classify unknown images they need to match the nearest neighbour in the stored database of extracted image features. In this paper, Discrete Cosine Transforms (DCTs) are used to reduce the dimensionality of image space by truncating high frequency DCT components. The remaining coefficients are fed into a neural network for classification. Because only a small number of low frequency DCT components are necessary to preserve the most important facial features such as hair outline, eyes and mouth, our DCT-based image recognition system is much faster than other approaches.
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