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<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.
Kyle Gallivan, Xiuwen Liu, Anuj Srivastava, "Optimal Linear Representations of Images for Object Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 26, no. , pp. 662-666, May 2004, doi:10.1109/TPAMI.2004.1273986
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