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.
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Viitaniemi, V., Laaksonen, J. (2009). Representing Images with χ 2 Distance Based Histograms of SIFT Descriptors. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_70
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DOI: https://doi.org/10.1007/978-3-642-04277-5_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04276-8
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