Texture classification by multi-predicate local binary pattern operators

M. Topi; P. Matti; O. Timo

doi:10.1109/icpr.2000.903699

November 11, 2002

Texture classification by multi-predicate local binary pattern operators

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Key Points

This research aims to enhance texture classification in images by introducing a multi-predicate local binary pattern approach.
Benchmarking the original local binary pattern approach against the multi-predicate version.
Focusing on supervised texture segmentation problems as outlined in a prior comparative study.
Analyzing performance on images with textures across multiple scales.
The multi-predicate LBP outperforms the simple LBP in segmenting textures.
Improved accuracy in identifying textures at different scales, enhancing overall image processing capabilities.

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Implication

Study benchmarks texture segmentation using multi-predicate local binary patterns, suggesting improvements for image analysis.

Abstract

This paper benchmarks the simple local binary pattern (LBP) approach in the supervised texture segmentation problems of the recent comparative study of Randen and Husoy (1999). A multi-predicate version of LBP is proposed, making our approach even more powerful for images containing textures at multiple scales.

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Topi et al. (2002) studied this question.

synapsesocial.com/papers/6a154840cb0379474a821779 https://doi.org/10.1109/icpr.2000.903699

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Texture classification by multi-predicate local binary pattern operators

Key Points

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Implication

Abstract

Discussion

Cite This Study

Texture classification by multi-predicate local binary pattern operators

Key Points

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Implication

Abstract

Discussion

Cite This Study

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