Multiresolution grayscale and rotation invariant texture classification with local binary patterns

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Multiresolution grayscale and rotation invariant texture classification with local binary patterns (2002)

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by Timo Ojala , Matti Pietikäinen , Topi Mäenpää

Venue:	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Citations:	1299 - 39 self

BibTeX

@ARTICLE{Ojala02multiresolutiongrayscale,
    author = {Timo Ojala and Matti Pietikäinen and Topi Mäenpää},
    title = {Multiresolution grayscale and rotation invariant texture classification with local binary patterns},
    journal = {IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE},
    year = {2002},
    volume = {24},
    number = {7},
    pages = {2002}
}

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Abstract

This paper presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Excellent experimental results obtained in true problems of rotation invariance, where the classifier is trained at one particular rotation angle and tested with samples from other rotation angles, demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns. These operators characterize the spatial configuration of local image texture and the performance can be further improved by combining them with rotation invariant variance measures that characterize the contrast of local image texture. The joint distributions of these orthogonal measures are shown to be very powerful tools for rotation invariant texture analysis.

Keyphrases

rotation invariant texture classification local binary pattern local image texture multiresolution grayscale multiple operator particular rotation angle lookup table powerful texture feature joint distribution multiresolution analysis excellent experimental result fundamental property rotation angle gray-scale variation monotonic transformation angular space multiresolution approach computational simplicity prototype distribution rotation invariant texture analysis occurrence histogram rotation invariant variance measure good discrimination small neighborhood true problem certain local binary pattern occurrence statistic spatial configuration powerful tool rotation invariance rotation invariant operator presentation nonparametric discrimination uniform pattern orthogonal measure spatial resolution generalized gray-scale gray scale

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