COMPARATIVE STUDY
JOURNAL ARTICLE

Optimal linear representations of images for object recognition

Xiuwen Liu, Anuj Srivastava, Kyle Gallivan
IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26 (5): 662-6
15460288
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.

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