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arxiv: 1906.11010 · v1 · pith:AOR3YQHInew · submitted 2019-06-26 · 💻 cs.CV

Color Texture Classification Based on Proposed Impulse-Noise Resistant Color Local Binary Patterns and Significant Points Selection Algorithm

Shervan Fekri-Ershad , Farshad Tajeripour This is my paper

Pith reviewed 2026-05-25 15:55 UTC · model grok-4.3

classification 💻 cs.CV
keywords color texture classificationlocal binary patternsnoise resistancesignificant points selectionhybrid color LBPtexture analysisimage classification
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The pith

A noise-resistant hybrid color local binary pattern combined with significant point selection achieves highest accuracy in color texture classification.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes a two-step approach to classify color textures by jointly using color information and texture features. It first defines a hybrid color local binary patterns operator that applies an AND operation across color channels to resist impulse noise. It then introduces a significant points selection algorithm that picks key texture points to cut computational cost while raising accuracy. Tests on Vistex, Outex, and KTH TIPS2a datasets show this combination beats prior methods on accuracy, noise tolerance, and speed, with built-in rotation invariance and multi-resolution handling. The work matters for any image task where textures must be recognized reliably under real-world noise.

Core claim

The central claim is that a new hybrid color local binary patterns operator, formed by combining color sensor data with AND operations to reduce noise sensitivity, paired with a significant points selection algorithm that retains only key LBP codes, produces the highest classification accuracy on standard texture datasets while also showing lower noise sensitivity and lower computational complexity than earlier LBP versions. The method is rotation invariant, supports multiple resolutions, and offers general usability for joint color and texture extraction in image processing.

What carries the argument

Hybrid color local binary patterns (HCLBP), which extends standard LBP to color channels via AND combination for impulse-noise resistance, together with the significant points selection algorithm that reduces the number of LBP codes processed.

If this is right

  • The approach can extract joint color and texture features for other image processing tasks such as material identification or retrieval.
  • Lower noise sensitivity allows reliable use on images captured with imperfect sensors.
  • Reduced computational complexity supports deployment where processing time or power is limited.
  • Rotation invariance and multi-resolution properties extend applicability to images taken at varying orientations and scales.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The point selection step might transfer to other local descriptors to achieve similar complexity reductions.
  • Testing the same pipeline on video sequences could reveal whether temporal consistency adds further gains.
  • Modern large-scale datasets could be used to check if the accuracy advantage scales beyond the three evaluated collections.

Load-bearing premise

That combining color sensor information using AND operation on the proposed noise resistant color LBP decreases sensitivity to noise without losing critical texture discrimination power.

What would settle it

Apply the method and standard color LBP to a held-out texture dataset containing controlled levels of impulse noise and measure whether accuracy drops less for the proposed version while keeping overall classification rates higher.

read the original abstract

The main aim of this paper is to propose a color texture classification approach which uses color sensor information and texture features jointly. High accuracy, low noise sensitivity and low computational complexity are specified aims for our proposed approach. One of the efficient texture analysis operations is local binary patterns. The proposed approach includes two steps. First, a noise resistant version of color local binary patterns is proposed to decrease sensitivity to noise of LBP. This step is evaluated based on combination of color sensor information using AND operation. In second step, a significant points selection algorithm is proposed to select significant LBP. This phase decreases final computational complexity along with increasing accuracy rate. The Proposed approach is evaluated using Vistex, Outex, and KTH TIPS2a data sets. Our approach has been compared with some state of the art methods. It is experimentally demonstrated that the proposed approach achieves highest accuracy. In two other experiments, result show low noise sensitivity and low computational complexity of the proposed approach in comparison with previous versions of LBP. Rotation invariant, multi resolution, general usability are other advantages of our proposed approach. In the present paper, a new version of LBP is proposed originally, which is called Hybrid color local binary patterns. It can be used in many image processing applications to extract color and texture features jointly. Also, a significant point selection algorithm is proposed for the first time to select key points of images.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes a two-step color texture classification method: (1) a noise-resistant hybrid color local binary patterns (LBP) operator that fuses color channels via AND to reduce impulse-noise sensitivity, and (2) a significant-points selection algorithm to reduce computational cost while improving accuracy. The approach is evaluated on the Vistex, Outex, and KTH-TIPS2a datasets, with claims of highest accuracy versus prior LBP variants, low noise sensitivity, and reduced complexity; additional advantages cited include rotation invariance and multi-resolution applicability.

Significance. If the experimental claims hold under rigorous controls, the hybrid color LBP construction and the significant-points algorithm would constitute practical contributions to texture analysis in noisy or resource-constrained settings. The explicit focus on joint color-texture extraction and the inclusion of noise-sensitivity and complexity experiments are strengths that could support adoption in image-processing pipelines.

major comments (3)
  1. [Method description and experimental evaluation] The central claim that AND-based fusion of the proposed impulse-noise-resistant color LBP channels decreases noise sensitivity without losing critical texture discrimination power is load-bearing, yet the experiments report only end-to-end accuracy and aggregate noise tests; no ablation isolating AND versus OR, sum, or per-channel concatenation appears in the results.
  2. [Experimental results] The claim that the proposed approach achieves the highest accuracy is presented without quantitative values, error bars, or explicit baseline implementation details (parameter settings, training protocols) in the reported comparisons, undermining verification of superiority on the three datasets.
  3. [Significant points selection algorithm and results] The significant-points selection algorithm is asserted to increase accuracy while decreasing complexity, but no analysis of the number of retained points, selection threshold, or accuracy-complexity trade-off curve is provided to substantiate the dual benefit.
minor comments (2)
  1. [Abstract] Abstract contains grammatical issues (e.g., 'result show' should be 'results show') and lacks any numerical accuracy or complexity figures despite stating experimental superiority.
  2. [Method] Notation for the hybrid color LBP operator and the precise definition of the AND fusion step should be formalized with equations for reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight areas where additional experimental detail would strengthen the paper. We address each major comment below and will incorporate the requested analyses in the revised manuscript.

read point-by-point responses

  1. Referee: [Method description and experimental evaluation] The central claim that AND-based fusion of the proposed impulse-noise-resistant color LBP channels decreases noise sensitivity without losing critical texture discrimination power is load-bearing, yet the experiments report only end-to-end accuracy and aggregate noise tests; no ablation isolating AND versus OR, sum, or per-channel concatenation appears in the results.

    Authors: We agree that an explicit ablation comparing the AND fusion operator against OR, summation, and per-channel concatenation would better isolate its contribution to noise resistance. The revised manuscript will include this ablation study on the Vistex, Outex, and KTH-TIPS2a datasets, reporting accuracy under controlled impulse noise levels for each fusion variant. revision: yes

  2. Referee: [Experimental results] The claim that the proposed approach achieves the highest accuracy is presented without quantitative values, error bars, or explicit baseline implementation details (parameter settings, training protocols) in the reported comparisons, undermining verification of superiority on the three datasets.

    Authors: The original manuscript contains comparison tables, but we acknowledge that tabulated numerical accuracies, standard deviations (or error bars from repeated trials), and complete baseline parameter/training details were not sufficiently explicit. In the revision we will expand the tables with all requested quantitative values, error statistics, and implementation protocols to enable direct verification. revision: yes

  3. Referee: [Significant points selection algorithm and results] The significant-points selection algorithm is asserted to increase accuracy while decreasing complexity, but no analysis of the number of retained points, selection threshold, or accuracy-complexity trade-off curve is provided to substantiate the dual benefit.

    Authors: We recognize that quantifying the trade-off is necessary. The revised version will report the number of retained points for varying selection thresholds, together with accuracy-versus-complexity curves (accuracy vs. retained points and vs. measured runtime) on the three datasets to substantiate both the accuracy gain and complexity reduction. revision: yes

Circularity Check

0 steps flagged

No circularity: new LBP operator and selection algorithm are independent constructions evaluated on external data

full rationale

The paper proposes an impulse-noise resistant color LBP variant (using AND fusion of color channels) and a significant points selection algorithm as original algorithmic constructions. Performance claims (highest accuracy, low noise sensitivity, low complexity) are obtained by direct empirical comparison on Vistex, Outex, and KTH TIPS2a datasets against prior LBP variants and state-of-the-art methods. No equations, fitted parameters, or self-citations are invoked to derive these outcomes; the method steps are presented as novel and then measured end-to-end. The AND-fusion assumption is not justified by prior self-work but is part of the proposed construction whose effect is assessed via the reported experiments. This is a standard empirical method paper with self-contained evaluation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claim rests on the domain assumption that AND combination of color channels yields noise resistance and that point selection improves both accuracy and complexity; no free parameters or invented physical entities are specified in the abstract.

axioms (1)
  • domain assumption AND operation on color sensor channels decreases sensitivity to impulse noise
    Invoked in the first step of the proposed approach as the mechanism for noise resistance.
invented entities (2)
  • Hybrid color local binary patterns no independent evidence
    purpose: Joint extraction of color and texture features with noise resistance
    Newly proposed variant; no independent evidence outside the paper's experiments.
  • Significant points selection algorithm no independent evidence
    purpose: Select key LBP points to reduce complexity while increasing accuracy
    Newly proposed; no independent evidence outside the paper's experiments.

pith-pipeline@v0.9.0 · 5790 in / 1239 out tokens · 23202 ms · 2026-05-25T15:55:26.696800+00:00 · methodology

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Reference graph

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