Article
Computer Science, Artificial Intelligence
Qiwu Luo, Jiaojiao Su, Chunhua Yang, Olli Silven, Li Liu
Summary: In this paper, a novel image descriptor, called SNELBP, is proposed to address scale transformation and noise interference simultaneously. It achieves competitive results compared to classical LBP variants and typical deep learning methods.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Information Systems
Yijie Luo, Jiming Sa, Yuyan Song, He Jiang, Chi Zhang, Zhushanying Zhang
Summary: This paper proposes an improved LBP operator by using a local binary pattern operator based on magnitude ranking and a global threshold segmentation operator, to further improve the performance. This improved LBP achieves excellent texture classification accuracy across six common datasets, with an average of 1% lower than the best LBP variants. Meanwhile, the computational complexity of the proposed improved LBP is several times lower than that of the best LBP variants.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Xin Shu, Zhigang Song, Jinlong Shi, Shucheng Huang, Xiao-Jun Wu
Summary: The paper introduces a novel method, multiple channels local binary pattern (MCLBP), which combines single-channel texture characteristics with multi-channel color information for color texture representation and classification. Results from comprehensive experiments on benchmark databases show that this method outperforms most existing color texture features in terms of classification accuracy.
SIGNAL PROCESSING-IMAGE COMMUNICATION
(2021)
Article
Computer Science, Software Engineering
Shaokun Lan, Jie Li, Shiqi Hu, Hongcheng Fan, Zhibin Pan
Summary: In this paper, a neighbourhood feature-based local binary pattern (NF-LBP) is proposed to improve the classification performance of the local binary pattern (LBP) in texture feature analysis. The NF-LBP method combines the neighbourhood feature, local sign component, and centre pixel component to provide better texture information and is robust to noise.
Article
Computer Science, Artificial Intelligence
Xin Shu, Hui Pan, Jinlong Shi, Xiaoning Song, Xiao-Jun Wu
Summary: This paper proposes a novel global refined local binary pattern (GRLBP) for texture feature extraction. By analyzing the nature of pixel intensity distribution in local neighborhoods, GRLBP can effectively describe and distinguish local neighborhoods with similar structures but different contrasts or grayscales. Experimental results demonstrate that GRLBP can represent detailed information of texture images and outperforms state-of-the-art LBP variants in terms of classification accuracy, feature dimension, and computational complexity.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Information Systems
Zeng Qiang, Adu Jianhua, Sun Xiaoya, Hong Sunyan
Summary: An extended complete LBP (ELBP) method is proposed for texture classification in this paper, which provides a detailed description and analysis of the composition of local feature vectors. Experimental results show that the algorithm has good scalability and robustness.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Nuh Alpaslan
Summary: This paper presents novel hybrid methods based on neutrosophic set and LBP features. By transforming the input image into a neutrosophic domain and combining with grayscale images, the proposed methods can extract more robust features. The methods contribute to the classification performance with reasonable computational cost and achieve satisfactory results in experiments.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Tiecheng Song, Jie Feng, Lin Luo, Chenqiang Gao, Hongliang Li
Summary: In this paper, two novel operators, local grouped order pattern (LGOP) and non-local binary pattern (NLBP), are proposed for texture description. Experimental results demonstrate that combining LGOP and NLBP to construct discriminative histogram features as texture descriptor LGONBP shows superiority over state-of-the-art LBP variants for texture classification.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
S. Nithya, S. Ramakrishnan
Summary: This paper proposes a new texture classification method called wavelet domain majority coupled binary pattern, which achieves efficient image retrieval using wavelet transform and binary patterns, and demonstrates superior performance in experiments.
PATTERN ANALYSIS AND APPLICATIONS
(2021)
Article
Materials Science, Multidisciplinary
Ibtissam Al Saidi, Mohammed Rziza, Johan Debayle
Summary: CPLBP is a novel texture classification descriptor that extends the neighborhood region using polar coordinates, enhancing feature extraction efficiency. By dividing the circle and calculating the average value of each part, CPLBP captures discriminating relationships among pixels in the local neighborhood effectively.
IMAGE ANALYSIS & STEREOLOGY
(2021)
Article
Green & Sustainable Science & Technology
Saeed Aligholi, Reza Khajavi, Manoj Khandelwal, Danial Jahed Armaghani
Summary: In this paper, a rotation-invariant local binary pattern operator equipped with a local contrast measure is proposed to characterize the type of mineral twinning by inspecting the texture properties of crystals. The proposed system achieves high sensitivity and accuracy in texture identification and can be applied in various fields for classification and feature recognition tasks.
Article
Computer Science, Information Systems
Shekhar Karanwal
Summary: In Local Binary Pattern (LBP), the small differences between pixels can cause image noise and decrease recognition rate. To overcome this, novel descriptors are introduced to improve robustness and discriminativity. These descriptors compare pixels in different ways and construct features based on thresholding functions. By combining these features, challenges such as lighting, pose, emotion, and scale can be addressed.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Acoustics
Mehmet Bilal Er
Summary: Cardiovascular diseases are a serious health threat with costly and time-consuming diagnostic methods. A new heart sound classification model based on deep learning is proposed, which achieves high accuracy rates by combining feature extraction, hybridization, feature selection, and one-dimensional convolutional neural network classification. The results surpass current methods in terms of classification accuracy.
Article
Computer Science, Information Systems
Entessar Saeed Gemeay, Farhan A. Alenizi, Adil Hussein Mohammed, Mohammad Hossein Shakoor, Reza Boostani
Summary: This paper discusses several statistical descriptors for feature extraction from texture images, and introduces the local binary pattern as one of the most popular descriptors. A weighted constraint feature selection approach is proposed in this paper to select a small number of features without degrading the classification accuracy, which significantly improves the classification rate.
Article
Computer Science, Theory & Methods
Sayed Mohamad Tabatabaei, Abdolah Chalechale
Summary: This paper proposes a noise robust texture descriptor MACCBP, which applies a novel mechanism for potential noisy central pixel detection and correction, and uses a new sampling method to correct potential noisy neighboring pixels. The proposed descriptor achieves high classification accuracy in both noisy and noiseless environments.
MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING
(2022)