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Optimal linear representations of images for object recognition | IEEE Journals & Magazine | IEEE Xplore

Optimal linear representations of images for object recognition


Abstract:

Although linear representations are frequently used in image analysis, their performances are seldom optimal in specific applications. This paper proposes a stochastic gr...Show More

Abstract:

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.
Page(s): 662 - 666
Date of Publication: 31 May 2004

ISSN Information:

PubMed ID: 15460288

References

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