Robust Adaptive Median Binary Pattern for noisy texture classification and retrieval
Mohammad Alkhatib, Adel Hafiane

TL;DR
This paper introduces RAMBP, a new texture descriptor that robustly classifies and retrieves noisy textures by analyzing micro and macrostructure features without prior noise knowledge.
Contribution
The paper presents RAMBP, a novel adaptive binary pattern descriptor that effectively handles high noise levels in texture classification and retrieval tasks, outperforming existing methods.
Findings
Achieved over 90% accuracy under 50% impulse noise.
Scored above 95% under Gaussian noise with σ=5.
Reached over 99% accuracy under Gaussian blur with σ=1.25.
Abstract
Texture is an important cue for different computer vision tasks and applications. Local Binary Pattern (LBP) is considered one of the best yet efficient texture descriptors. However, LBP has some notable limitations, mostly the sensitivity to noise. In this paper, we address these criteria by introducing a novel texture descriptor, Robust Adaptive Median Binary Pattern (RAMBP). RAMBP based on classification process of noisy pixels, adaptive analysis window, scale analysis and image regions median comparison. The proposed method handles images with high noisy textures, and increases the discriminative properties by capturing microstructure and macrostructure texture information. The proposed method has been evaluated on popular texture datasets for classification and retrieval tasks, and under different high noise conditions. Without any train or prior knowledge of noise type, RAMBP…
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