Quaternionic extended local binary pattern with adaptive structural pyramid pooling for color image representation

Song, Tianyou; Xin, Liangliang; Gao, Chenqiang; Zhang, Tianqi; Huang, Yao

doi:10.1016/j.patcog.2021.107891

Cited by 18 publications

(4 citation statements)

References 41 publications

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“…1D-TP is developed based on the local ternary pattern [33,34], which is generally used in image processing. Different from directly processing multi-dimensional matrices -571 in local ternary mode, the patterns obtained from the comparisons between two neighbours of the vibration signals are adopted in 1D-TP.…”

Section: One-dimensional Ternary Patternsmentioning

confidence: 99%

An improved bearing fault detection strategy based on artificial bee colony algorithm

Wang

Yue

et al. 2022

CAAI Trans on Intel Tech

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The operating state of bearing affects the performance of rotating machinery; thus, how to accurately extract features from the original vibration signals and recognise the faulty parts as early as possible is very critical. In this study, the one‐dimensional ternary model which has been proved to be an effective statistical method in feature selection is introduced and shapelet transformation is proposed to calculate the parameter of one‐dimensional ternary model that is usually selected by trial and error. Then XGBoost is used to recognise the faults from the obtained features, and artificial bee colony algorithm (ABC) is introduced to optimise the parameters of XGBoost. Moreover, for improving the performance of intelligent algorithm, an improved strategy where the evolution is guided by the probability that the optimal solution appears in certain solution space is proposed. The experimental results based on the failure vibration signal samples show that the average accuracy of fault signal recognition can reach 97%, which is much higher than the ones corresponding to traditional extraction strategies. And with the help of improved ABC algorithm, the performance of XGBoost classifier could be optimised; the accuracy could be improved from 97.02% to 98.60% compared with the traditional classification strategy.

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Section: One-dimensional Ternary Patternsmentioning

confidence: 99%

An improved bearing fault detection strategy based on artificial bee colony algorithm

Wang

Yue

et al. 2022

CAAI Trans on Intel Tech

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“…During the LBP feature extraction task, the necessary LBP patterns are produced by assigning its weights as W = 1, 2, 3, and 4, and from these images, essential features with a dimension of 1 × 1 × 59 are extracted from every image. The LBP technique adopted in this work is found in [30][31][32]. During the DWT feature extraction process, the test images are processed with the DWT technique discussed in [33][34][35][36][37].…”

Section: Machine-learning-featuresmentioning

confidence: 99%

Automatic Detection of Tuberculosis Using VGG19 with Seagull-Algorithm

Mohan

Kadry

Rajinikanth

et al. 2022

Life

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Due to various reasons, the incidence rate of communicable diseases in humans is steadily rising, and timely detection and handling will reduce the disease distribution speed. Tuberculosis (TB) is a severe communicable illness caused by the bacterium Mycobacterium-Tuberculosis (M. tuberculosis), which predominantly affects the lungs and causes severe respiratory problems. Due to its significance, several clinical level detections of TB are suggested, including lung diagnosis with chest X-ray images. The proposed work aims to develop an automatic TB detection system to assist the pulmonologist in confirming the severity of the disease, decision-making, and treatment execution. The proposed system employs a pre-trained VGG19 with the following phases: (i) image pre-processing, (ii) mining of deep features, (iii) enhancing the X-ray images with chosen procedures and mining of the handcrafted features, (iv) feature optimization using Seagull-Algorithm and serial concatenation, and (v) binary classification and validation. The classification is executed with 10-fold cross-validation in this work, and the proposed work is investigated using MATLAB® software. The proposed research work was executed using the concatenated deep and handcrafted features, which provided a classification accuracy of 98.6190% with the SVM-Medium Gaussian (SVM-MG) classifier.

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“…To extract color LBP features, many works [21], [22] have been proposed to consider the interaction between pixel from different color components. In this work, we follow [15] to extract extend opponent color LBP (OCLBP), each image is then characterized by 9 LBP histograms.…”

Section: Local Binary Patternsmentioning

confidence: 99%

Data augmentation by combining feature selection and color features for image classification

Meethongjan

Hoang

Surinwarangkoon

2022

IJECE

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<span lang="EN-US">Image classification is an essential task in computer vision with various applications such as bio-medicine, industrial inspection. In some specific cases, a huge training data is required to have a better model. However, it is true that full label data is costly to obtain. Many basic pre-processing methods are applied for generating new images by translation, rotation, flipping, cropping, and adding noise. This could lead to degrade the performance. In this paper, we propose a method for data augmentation based on color features information combining with feature selection. This combination allows improving the classification accuracy. The proposed approach is evaluated on several texture datasets by using local binary patterns features.</span>

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Quaternionic extended local binary pattern with adaptive structural pyramid pooling for color image representation

Cited by 18 publications

References 41 publications

An improved bearing fault detection strategy based on artificial bee colony algorithm

An improved bearing fault detection strategy based on artificial bee colony algorithm

Automatic Detection of Tuberculosis Using VGG19 with Seagull-Algorithm

Data augmentation by combining feature selection and color features for image classification

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