Kernel Codebooks for Scene Categorization

Authors:
Jan C. Gemert;Jan-Mark Geusebroek;Cor J. Veenman;Arnold W. Smeulders
Affiliations:
Intelligent Systems Lab Amsterdam (ISLA), University of Amsterdam, Amsterdam, The Netherlands 1098 SJ;Intelligent Systems Lab Amsterdam (ISLA), University of Amsterdam, Amsterdam, The Netherlands 1098 SJ;Intelligent Systems Lab Amsterdam (ISLA), University of Amsterdam, Amsterdam, The Netherlands 1098 SJ;Intelligent Systems Lab Amsterdam (ISLA), University of Amsterdam, Amsterdam, The Netherlands 1098 SJ
Venue:
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Year:
2008

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Abstract

This paper introduces a method for scene categorization by modeling ambiguity in the popular codebook approach. The codebook approach describes an image as a bag of discrete visual codewords, where the frequency distributions of these words are used for image categorization. There are two drawbacks to the traditional codebook model: codeword uncertainty and codeword plausibility. Both of these drawbacks stem from the hard assignment of visual features to a single codeword. We show that allowing a degree of ambiguity in assigning codewords improves categorization performance for three state-of-the-art datasets.