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A Novel Interaction Protocol of a Multiagent System for the Study of Alternative Decisions Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Avoiding the Curse of Dimensionality in Local Binary Patterns

Authors : Karel Petranek, Jan Vanek, Eva Milkova

Published in: Computational Collective Intelligence

Publisher: Springer International Publishing

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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.

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Metadata
Title
Avoiding the Curse of Dimensionality in Local Binary Patterns
Authors
Karel Petranek
Jan Vanek
Eva Milkova
Copyright Year
2016
Book
Computational Collective Intelligence

Print ISBN: 978-3-319-45242-5

Electronic ISBN: 978-3-319-45243-2

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-45243-2_19

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