Recognition with Local Features: The Kernel Recipe

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Recognition with Local Features: The Kernel Recipe (2003)

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by Christian Wallraven

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178 - 26 self

BibTeX

@INPROCEEDINGS{Wallraven03recognitionwith,
    author = {Christian Wallraven},
    title = {Recognition with Local Features: The Kernel Recipe},
    booktitle = {},
    year = {2003},
    pages = {257--264}
}

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Abstract

Recent developments in computer vision have shown that local features can provide efficient representations suitable for robust object recognition. Support Vector Machines have been established as powerful learning algorithms with good generalization capabilities. In this paper, we combine these two approaches and propose a general kernel method for recognition with local features. We show that the proposed kernel satisfies the Mercer condition and that it is suitable for many established local feature frameworks. Large-scale recognition results are presented on three different databases, which demonstrate that SVMs with the proposed kernel perform better than standard matching techniques on local features. In addition, experiments on noisy and occluded images show that local feature representations significantly outperform global approaches. 1.

Keyphrases

local feature kernel recipe robust object recognition different database support vector machine efficient representation local feature representation recent development large-scale recognition result good generalization capability computer vision kernel perform local feature framework global approach mercer condition general kernel method

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