Categorizing Nine Visual Classes using Local Appearance Descriptors
Jutta Willamowski, Damian Arregui, Gabriela Csurka, Chris Dance, Lixin Fan
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 intrinsically invariant. We present results for classifying nine semantic visual categories and comment on results obtained by Fergus et al using a different method on the same data set. We obtain excellent results as well for multi class categorization as for object detection. A thorough evaluation clearly demonstrates that our method is robust to background clutter and produces good categorization accuracy even without exploiting geometric information.
ICPR 2004 Workshop Learning for Adaptable Visual Systems Cambridge, United Kingdom 22 August, 2004.
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