Multiple channels local binary pattern for color texture representation and classification

2021 ◽  
pp. 116392
Author(s):  
Xin Shu ◽  
Zhigang Song ◽  
Jinlong Shi ◽  
Shucheng Huang ◽  
Xiao-Jun Wu
Keyword(s):  
Local Binary Pattern ◽  
Multiple Channels ◽  
Texture Representation ◽  
Color Texture

scholarly journals Unsupervised Local Binary Pattern Histogram Selection Scores for Color Texture Classification

2018 ◽  
Vol 4 (10) ◽  
pp. 112 ◽  
Author(s):  
Mariam Kalakech ◽  
Alice Porebski ◽  
Nicolas Vandenbroucke ◽  
Denis Hamad
Keyword(s):  
Local Binary Pattern ◽  
Color Image ◽  
Texture Classification ◽  
Classification Problems ◽  
Class Label ◽  
Laplacian Score ◽  
Pattern Histogram ◽  
Color Texture

These last few years, several supervised scores have been proposed in the literature to select histograms. Applied to color texture classification problems, these scores have improved the accuracy by selecting the most discriminant histograms among a set of available ones computed from a color image. In this paper, two new scores are proposed to select histograms: The adapted Variance score and the adapted Laplacian score. These new scores are computed without considering the class label of the images, contrary to what is done until now. Experiments, achieved on OuTex, USPTex, and BarkTex sets, show that these unsupervised scores give as good results as the supervised ones for LBP histogram selection.

Colored spun fabric texture representation and application by combining spatial features with frequency features

2020 ◽  
pp. 004051752096140
Author(s):  
Li Yuan ◽  
Xue Gong ◽  
Junping Liu ◽  
Yali Yang ◽  
Muli Liu
Keyword(s):  
Classification Accuracy ◽  
Local Binary Pattern ◽  
Learning Algorithm ◽  
Woven Fabric ◽  
Local Phase ◽  
Texture Representation ◽  
Local Phase Quantization ◽  
Fuzzy Transformation ◽  
Representation Model ◽  
Phase Quantization

Colored spun fabrics are difficult to accurately characterize with a local binary pattern due to texture anisotropy caused by the uneven distribution of dyed fibers. In this paper, we present a texture representation model based on spatial and frequency characteristics. The proposed model takes advantage of the local binary pattern and local phase quantization to extract the texture of woven fabric. Then, the two features are connected in series, and the features of dimension reduction by principal component analysis are used to represent the texture of the fabric image. Finally, the hierarchical hybrid classifier is applied to classify the fabric structure. The experimental results show that the local phase quantization feature is robust to the fuzzy transformation and the texture representation model has a stronger ability of texture description than the single local binary pattern feature, with the average classification accuracy of 97.59% on 336 samples. In addition, compared with the deep learning algorithm, the texture representation algorithm can ensure a high classification accuracy.

Color texture representation using multiscale feature boundaries

1992 ◽  
Author(s):  
Jacob Scharcanski ◽  
Jeff K. Hovis ◽  
Helen C. Shen
Keyword(s):  
Texture Representation ◽  
Color Texture

scholarly journals Combination of LBP Bin and Histogram Selections for Color Texture Classification

2020 ◽  
Vol 6 (6) ◽  
pp. 53
Author(s):  
Alice Porebski ◽  
Vinh Truong Hoang ◽  
Nicolas Vandenbroucke ◽  
Denis Hamad
Keyword(s):  
Local Binary Pattern ◽  
Texture Classification ◽  
Selection Procedure ◽  
Texture Features ◽  
Feature Space ◽  
Selection Methods ◽  
Texture Descriptor ◽  
Classification Problems ◽  
The Past ◽  
Color Texture

LBP (Local Binary Pattern) is a very popular texture descriptor largely used in computer vision. In most applications, LBP histograms are exploited as texture features leading to a high dimensional feature space, especially for color texture classification problems. In the past few years, different solutions were proposed to reduce the dimension of the feature space based on the LBP histogram. Most of these approaches apply feature selection methods in order to find the most discriminative bins. Recently another strategy proposed selecting the most discriminant LBP histograms in their entirety. This paper tends to improve on these previous approaches, and presents a combination of LBP bin and histogram selections, where a histogram ranking method is applied before processing a bin selection procedure. The proposed approach is evaluated on five benchmark image databases and the obtained results show the effectiveness of the combination of LBP bin and histogram selections which outperforms the simple LBP bin and LBP histogram selection approaches when they are applied independently.

scholarly journals Face Antispoofing Method Using Color Texture Segmentation on FPGA

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Youngjun Moon ◽  
Intae Ryoo ◽  
Seokhoon Kim
Keyword(s):  
Local Binary Pattern ◽  
User Authentication ◽  
Color Difference ◽  
Face Images ◽  
Texture Information ◽  
Biometric Systems ◽  
Spoofing Detection ◽  
Real World Applications ◽  
Face Spoofing ◽  
Color Texture

User authentication for accurate biometric systems is becoming necessary in modern real-world applications. Authentication systems based on biometric identifiers such as faces and fingerprints are being applied in a variety of fields in preference over existing password input methods. Face imaging is the most widely used biometric identifier because the registration and authentication process is noncontact and concise. However, it is comparatively easy to acquire face images using SNS, etc., and there is a problem of forgery via photos and videos. To solve this problem, much research on face spoofing detection has been conducted. In this paper, we propose a method for face spoofing detection based on convolution neural networks using the color and texture information of face images. The color-texture information combined with luminance and color difference channels is analyzed using a local binary pattern descriptor. Color-texture information is analyzed using the Cb, S, and V bands in the color spaces. The CASIA-FASD dataset was used to verify the proposed scheme. The proposed scheme showed better performance than state-of-the-art methods developed in previous studies. Considering the AI FPGA board, the performance of existing methods was evaluated and compared with the method proposed herein. Based on these results, it was confirmed that the proposed method can be effectively implemented in edge environments.

Illumination and Rotation Invariant Texture Representation for Face Recognition

2020 ◽  
Vol 10 (2) ◽  
pp. 58-69
Author(s):  
Medha Kudari ◽  
Shivashankar S. ◽  
Prakash S. Hiremath
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Galois Field ◽  
Rotation Invariance ◽  
Invariance Property ◽  
Rotation Invariant ◽  
Texture Representation ◽  
Novel Approach ◽  
Local Gradient

This article presents a novel approach for illumination and rotation invariant texture representation for face recognition. A gradient transformation is used as illumination invariance property and a Galois Field for the rotation invariance property. The normalized cumulative histogram bin values of the Gradient Galois Field transformed image represent the illumination and rotation invariant texture features. These features are further used as face descriptors. Experimentations are performed on FERET and extended Cohn Kanade databases. The results show that the proposed method is better as compared to Rotation Invariant Local Binary Pattern, Log-polar transform and Sorted Local Gradient Pattern and is illumination and rotation invariant.

scholarly journals Texture Representation and Application of Colored Spun Fabric Using Uniform Three-Structure Descriptor

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yuan Li ◽  
Muli Liu ◽  
JunPing Liu ◽  
Yali Yang ◽  
Xue Gong
Keyword(s):  
Local Binary Pattern ◽  
Contextual Information ◽  
Texture Features ◽  
Correlation Index ◽  
Average Precision ◽  
Texture Representation ◽  
The Mean ◽  
Texture Structure ◽  
Different Color ◽  
Structure Descriptor

Abstract The local binary pattern (LBP) and its variants have shown their effectiveness in texture images representation. However, most of these LBP methods only focus on the histogram of LBP patterns, ignoring the spatial contextual information among them. In this paper, a uniform three-structure descriptor method was proposed by using three different encoding methods so as to obtain the local spatial contextual information for characterizing the nonuniform texture on the surface of colored spun fabrics. The testing results of 180 samples with 18 different color schemes indicate that the established texture representation model can accurately express the nonuniform texture structure of colored spun fabrics. In addition, the overall correlation index between texture features and sample parameters is 0.027 and 0.024, respectively. When compared with the LBP and its variants, the proposed method obtains a higher representational ability, and simultaneously owns a shorter time complexity. At the same time, the algorithm proposed in this paper enjoys ideal effectiveness and universality for fabric image retrieval. The mean Average Precision (mAP) of the first group of samples is 86.2%; in the second group of samples, the mAP of the sample with low twist coefficient is 89.6%, while the mAP of the sample with high twist coefficient is 88.5%.

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