Visual categorization with bags of keypoints

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Visual categorization with bags of keypoints (2004)

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by Gabriella Csurka , Christopher R. Dance , Lixin Fan , Jutta Willamowski , Cédric Bray

Venue:	In Workshop on Statistical Learning in Computer Vision, ECCV
Citations:	1005 - 14 self

BibTeX

@INPROCEEDINGS{Csurka04visualcategorization,
    author = {Gabriella Csurka and Christopher R. Dance and Lixin Fan and Jutta Willamowski and Cédric Bray},
    title = {Visual categorization with bags of keypoints},
    booktitle = {In Workshop on Statistical Learning in Computer Vision, ECCV},
    year = {2004},
    pages = {1--22}
}

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Abstract

Abstract. We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors of image patches. We propose and compare two alternative implementations using different classifiers: Naïve Bayes and SVM. The main advantages of the method are that it is simple, computationally efficient and intrinsically invariant. We present results for simultaneously classifying seven semantic visual categories. These results clearly demonstrate that the method is robust to background clutter and produces good categorization accuracy even without exploiting geometric information. 1.

Keyphrases

visual categorization semantic visual category main advantage affine invariant descriptor vector quantization object class image patch different classifier natural image geometric information alternative implementation novel method generic visual categorization object content good categorization accuracy present result

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