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Issue No.05 - May (2004 vol.26)
pp: 662-666
<p><b>Abstract</b>—Although linear representations are frequently used in image analysis, their performances are seldom optimal in specific applications. This paper proposes a stochastic gradient algorithm for finding optimal linear representations of images for use in appearance-based object recognition. Using the nearest neighbor classifier, a recognition performance function is specified and linear representations that maximize this performance are sought. For solving this optimization problem on a Grassmann manifold, a stochastic gradient algorithm utilizing intrinsic flows is introduced. Several experimental results are presented to demonstrate this algorithm.</p>
Optimal subspaces, Grassmann manifold, object recognition, linear representations, dimension reduction, optimal component analysis.
Anuj Srivastava, Kyle Gallivan, "Optimal Linear Representations of Images for Object Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.26, no. 5, pp. 662-666, May 2004, doi:10.1109/TPAMI.2004.1273986
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