Abstract
This paper proposes a new method of feature extraction called two-dimensional optimal transform (2D-OPT) useful for appearance based object recognition. The 2D-OPT method provides a better discrimination power between classes by maximizing the distance between class centers. We first argue that the proposed 2D-OPT method works in the row direction of images and subsequently we propose an alternate 2D-OPT which works in the column direction of images. To straighten out the problem of massive memory requirements of the 2D-OPT method and as well the alternate 2D-OPT method, we introduce bi-projection 2D-OPT. The introduced bi-projection 2D-OPT method has the advantage of higher recognition rate, lesser memory requirements and better computing performance than the standard PCA/2D-PCA/Generalized 2D-PCA method, and the same has been revealed through extensive experimentations conducted on COIL-20 dataset and AT&T face dataset.
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Shekar, B.H., Guru, D.S., Nagabhushan, P. (2006). Two-Dimensional Optimal Transform for Appearance Based Object Recognition. In: Kalra, P.K., Peleg, S. (eds) Computer Vision, Graphics and Image Processing. Lecture Notes in Computer Science, vol 4338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949619_58
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DOI: https://doi.org/10.1007/11949619_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68301-8
Online ISBN: 978-3-540-68302-5
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