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Avoiding the Curse of Dimensionality in Local Binary Patterns

  • Karel PetranekEmail author
  • Jan Vanek
  • Eva Milkova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9875)

Abstract

Local Binary Patterns is a popular grayscale texture operator used in computer vision for classifying textures. The output of the operator is a bit string of a defined length, usually 8, 16 or 24 bits, describing local texture features. We focus on the problem of succinctly representing the patterns using alternative means and compressing them to reduce the number of dimensions. These reductions lead to simpler connections of Local Binary Patterns with machine learning algorithms such as neural networks or support vector machines, improve computation speed and simplify information retrieval from images. We study the distribution of Local Binary Patterns in 100000 natural images and show the advantages of our reduction technique by comparing it to existing algorithms developed by Ojala et al. We have also confirmed Ojala’s findings about the uniform LBP proportions.

Keywords

Dimensionality reduction Local binary patterns Image analysis 

Notes

Acknowledgement

This paper was supported by the research project SPEV, University of Hradec Kralove, Faculty of Informatics and Management, 2016.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of Hradec KraloveHradec KraloveCzech Republic

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