Robust Adaptive Median Binary Pattern for noisy texture classification and retrieval

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 achieved the best classification compared to state-of-the-art techniques. It scored more than $90\%$ under $50\%$ impulse noise densities, more than $95\%$ under Gaussian noised textures with standard deviation $\sigma = 5$, and more than $99\%$ under Gaussian blurred textures with standard deviation $\sigma = 1.25$. The proposed method yielded competitive results and high performance as one of the best descriptors in noise-free texture classification. Furthermore, RAMBP showed also high performance for the problem of noisy texture retrieval providing high scores of recall and precision measures for textures with high levels of noise.