Representing Images with Χ2 Distance Based Histograms of SIFT Descriptors

Authors:
Ville Viitaniemi;Jorma Laaksonen
Affiliations:
Department of Information and Computer Science, Helsinki University of Technology, TKK, Finland FI-02015;Department of Information and Computer Science, Helsinki University of Technology, TKK, Finland FI-02015
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Year:
2009

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Abstract

Histograms of local descriptors such as SIFT have proven to be powerful representations of image content. Often the histograms are formed using a clustering algorithm that compares the SIFT descriptors with the Euclidean distance. In this paper we experimentally investigate the usefulness of basing the comparisons of the SIFT descriptors on the Χ 2 distance measure instead. The modified approach results in improved image category detection performance when it is incorporated into a Bag-of-Visual-Words type category detection system.