PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Categorizing Nine Visual Classes with Bags of Keypoints
Christopher Dance, Jutta Willamowski, Gabriela Csurka and Cedric Bray
In: Pascal Vision Workshop 2004, 3-5 May 2004, Montbonnot, France.

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 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.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:215
Deposited By:Chris Dance
Deposited On:23 November 2004