Abstract
Histograms of local features have proven to be powerful representations in image classification and object detection. In this paper we experimentally compare techniques for selecting histogram codebooks for the purpose of classifying 5000 images of PASCAL NoE VOC Challenge 2007 collection. We study some well-known unsupervised clustering algorithms in the task of histogram codebook generation when the classification is performed in post-supervised fashion on basis of histograms of interest point SIFT features. We also consider several methods for supervised codebook generation that exploit the knowledge of the image classes to be detected already when selecting the histogram bins.
Supported by the Academy of Finland in the Finnish Centre of Excellence in Adaptive Informatics Research project.
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Viitaniemi, V., Laaksonen, J. (2008). Experiments on Selection of Codebooks for Local Image Feature Histograms. In: Sebillo, M., Vitiello, G., Schaefer, G. (eds) Visual Information Systems. Web-Based Visual Information Search and Management. VISUAL 2008. Lecture Notes in Computer Science, vol 5188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85891-1_16
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DOI: https://doi.org/10.1007/978-3-540-85891-1_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85890-4
Online ISBN: 978-3-540-85891-1
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