Improved opponent color local binary patterns: an effective local image descriptor for color texture classification

Bianconi, Francesco; Bello-Cerezo, Raquel; Napoletano, Paolo

doi:10.1117/1.jei.27.1.011002

Cited by 40 publications

(35 citation statements)

References 52 publications

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“…Local Binary Patterns (LBP). Concatenation of rotation-invariant ('ri') LBP histograms computed at resolution 1px, 2px and 3px using non-interpolated eight-pixel neighbourhoods as detailed in [23].…”

Section: Image Descriptorsmentioning

confidence: 99%

Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images

Bianconi

Kather

Reyes-Aldasoro

2019

Lecture Notes in Computer Science

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This paper compares the effects of colour pre-processing on the classification performance of H&E-stained images. Variations in the tissue preparation procedures, acquisition systems, stain conditions and reagents are all source of artifacts that can affect negatively computerbased classification. Pre-processing methods such as colour constancy, transfer and deconvolution have been proposed to compensate the artifacts. In this paper we compare quantitatively the combined effect of six colour pre-processing procedures and 12 colour texture descriptors on patch-based classification of H&E-stained images. We found that colour pre-processing had negative effects on accuracy in most cases -particularly when used with colour descriptors. However, some pre-processing procedures proved beneficial when employed in conjunction with classic texture descriptors such as co-occurrence matrices, Gabor filters and Local Binary Patterns.

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Section: Image Descriptorsmentioning

confidence: 99%

Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images

Bianconi

Kather

Reyes-Aldasoro

2019

Lecture Notes in Computer Science

Self Cite

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“…For instance, it could be made more robust to the neighborhood distance by concatenating several MLBP histograms computed with different distances. Marginal LBPs [17] Moment LBPs [19] maLBP [20] Cusano LBPs [21] Lee LBPs [22] OBLBP [23] IOBLBP [24] MLBP (ours)…”

Section: Resultsmentioning

confidence: 99%

“…Bianconi et al [24] similarly consider both intra-and interchannel information but with a different thresholding scheme. Their improved OBLBP (IOBLBP) operator uses a local average value rather than the sole central pixel value as threshold:…”

Section: E Opponent Band Lbpsmentioning

confidence: 99%

Spatio-spectral binary patterns based on multispectral filter arrays for texture classification

et al. 2018

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To discriminate gray-level texture images, spatial texture descriptors can be extracted using the local binary pattern (LBP) operator. This operator has been extended to color images at the expense of increased memory and computation requirements. Some authors propose to compute texture descriptors directly from raw images provided through a Bayer color filter array, which both avoids the demosaicking step and reduces the descriptor size. Recently, multispectral snapshot cameras have emerged to sample more than three wavelength bands using a multispectral filter array. Such cameras provide a raw image in which a single spectral channel value is available at each pixel. In this paper we design a local binary pattern operator that jointly extracts the spatial and spectral texture information directly from a raw image. Extensive experiments on a large dataset show that the proposed descriptor has both reduced computation cost and high discriminative power with regard to classical LBP descriptors applied to demosaicked images.

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“…In addition, we provide the MSE values of 25 color quantization levels (A a* , A b* ) between the FLCQ and EICQ quantizers in the CIELAB color model on all six datasets in Appendix A. Table 5 reports the evaluations of the APR and ARR rates obtained by the proposed descriptors and a series of state-of-the-art color LBP descriptors including OCLBP [18], IOCLBP [19], maLBP [23] mdLBP [23], OC-LBP + CH [26], LPCP [27] on all six datasets. The best values are highlighted in bold.…”

Section: Comparison With Other Hierarchical Quantization Schemesmentioning

confidence: 99%

“…Therefore, the problem of extracting effective, robust and practical features has attracted an increasing number of researchers. Thanks to these pioneers' breakthroughs, many approaches [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] have been continuously proposed and extended for the task of feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR

Feng

Wei

et al. 2019

Applied Sciences

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With the advent of medical endoscopes, earth observation satellites and personal phones, content-based image retrieval (CBIR) has attracted considerable attention, triggered by its wide applications, e.g., medical image analytics, remote sensing, and person re-identification. However, constructing effective feature extraction is still recognized as a challenging problem. To tackle this problem, we first propose the five-level color quantizer (FLCQ) to acquire a color quantization map (CQM). Secondly, according to the anatomical structure of the human visual system, the color quantization map (CQM) is amalgamated with a local binary pattern (LBP) map to construct a local ternary cross structure pattern (LTCSP). Third, the LTCSP is further converted into the uniform local ternary cross structure pattern (LTCSPuni) and the rotation-invariant local ternary cross structure pattern (LTCSPri) in order to cut down the computational cost and improve the robustness, respectively. Finally, through quantitative and qualitative evaluations on face, objects, landmark, textural and natural scene datasets, the experimental results illustrate that the proposed descriptors are effective, robust and practical in terms of CBIR application. In addition, the computational complexity is further evaluated to produce an in-depth analysis.

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Improved opponent color local binary patterns: an effective local image descriptor for color texture classification

Cited by 40 publications

References 52 publications

Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images

Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images

Spatio-spectral binary patterns based on multispectral filter arrays for texture classification

Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR

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