Face retrieval using frequency decoded local descriptor

Dubey, Shiv Ram

doi:10.1007/s11042-018-7028-8

Cited by 28 publications

(7 citation statements)

References 56 publications

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“…For comparative purposes, various local descriptors were also used to extract features from the chest X-ray images for the purposes of COVID-19 detection. In total, eight well-known local texture descriptors were considered, namely LBP ( Ahonen et al, 2006 ), FDLBP ( Dubey, 2019 ), QLRBP (Lan et al, 2015), BGP ( Zhang et al, 2012 ), LPQ ( Ojansivu & Heikkilä, 2008 ), BSIF ( Kannala & Rahtu, 2012 ), CENTRIST ( Wu & Rehg, 2010 ), and PHOG ( Bosch et al, 2007 ).…”

Section: Experimental Work and Resultsmentioning

confidence: 99%

“…Test achievements for the fine-tuning of the ResNet50 model and end-to-end training of the developed CNN model were found to be 92.6% and 91.6%, respectively. For comparative purposes, various local texture descriptors were considered; namely, Local Binary Patterns (LBP) ( Ahonen, Hadid, & Pietikainen, 2006 ), Frequency Decoded LBP (FDLBP) ( Dubey, 2019 ), Quaternionic Local Ranking Binary Pattern (QLRBP) (Lan, Zhou, & Tang, 2015), Binary Gabor Pattern (BGP) ( Zhang, Zhou, & Li, 2012 ), Local Phase Quantization (LPQ) ( Ojansivu & Heikkilä, 2008 ), Binarized Statistical Image Features (BSIF) ( Kannala & Rahtu, 2012 ), CENsus TRansform hISTogram (CENTRIST) ( Wu & Rehg, 2010 ), and Pyramid Histogram of Oriented Gradients (PHOG) ( Bosch, Zisserman, & Munoz, 2007 ). From the local texture descriptors, the BSIF with SVM classifier produced a 90.5% accuracy score.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning approaches for COVID-19 detection based on chest X-ray images

Ismael

Şengür

2021

Expert Systems with Applications

597

366

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Section: Experimental Work and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning approaches for COVID-19 detection based on chest X-ray images

Ismael

Şengür

2021

Expert Systems with Applications

597

366

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show abstract

“…Figure 4 shows flow of FDLBP. In this method [11] one low pass filter i.e. an average filter that produces the coarse information and four high pass filters horizontal-vertical difference filter, diagonal difference filter, Sobel vertical edge filter, Sobel horizontal edge filter which produces the detailed information.…”

Section: Frequency Decoded Local Binary Pattern (Fdlbp)mentioning

confidence: 99%

Comparative Analysis of Texture Patterns on Mammograms for Classification

Darapureddy¹,

Karatapu²,

Krishna³

2021

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Breast cancer is a cancerous tumor that arrives within the tissues of the breast. Women are mostly attacked than men. To detect early cancer medical specialists, suggest mammography for screening. Algorithms in Machine learning were executed on mammogram images to classify whether the tissues are deleterious or not. An analysis is done based on the texture feature extraction using different techniques like Frequency decoded local binary pattern (FDLBP), Local Bit-plane Decoded Pattern (LBDP), Local Diagonal Extrema Pattern (LDEP), Local Directional Order Pattern (LDOP), Local Wavelet Pattern (LWP). The features extracted are tested on 322 images from MIA’s database of three different classes. The algorithms in Machine learning like K-Nearest Neighbor classifier (KNN), Support vector classifier (SVC), Decision Tree classifier (DTC), Random Forest classifier (RFC), AdaBoost classifier (AC), Gradient Boosting classifier (GBC), Gaussian Naive Bayes classifier (GNB), Linear Discriminant Analysis classifier (LDA), Quadratic Discriminant Analysis classifier (QDA) were used to evaluate the accuracy of classification. This paper examines the comparison of accuracy using different texture features. KNN algorithm with LDEP for texture feature extraction gives classification accuracy of 64.61%, SVC with all the texture patterns mentioned gives classification accuracy of 63.07%, DTC with FDLBP, LBDP gives classification accuracy of 47.69, RFC with LBDP and AC with LDOP and GBC with FDLBP gives 61.53% classification accuracy, GNB and LDA with FDLBP gives 60% and 63.07% classification accuracy respectively, QDA with LBDP gives 64.61 classification accuracy. Of all the Algorithms support vector classifier gives good accuracy results with all the texture patterns mentioned.

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“…QLRBP was developed for local feature extraction based on Quaternion [14]. Quaternion is a complex number, which contains three imaginary and one real part.…”

Section: Quaternionic Local Ranking Binary Pattern (Qlrbp)mentioning

confidence: 99%

“…In particular, we aim to investigate the discrimination potential of local feature descriptors for ECG beat classification. Eight well-known local feature descriptors namely Local Binary Patterns (LBP) [13], Frequency Decoded LBP (FDLBP) [14], Quaternionic Local Ranking Binary Pattern (QLRBP) [15], Binary Gabor Pattern (BGP) [16], Local Phase Quantization (LPQ) [17], Binarized Statistical Image Features (BSIF) Abdullah et al Health Inf Sci Syst (2020) 8:20 [18], CENsus TRansform hISTogram (CENTRIST) [19] and Pyramid Histogram of Oriented Gradients (PHOG) [20] are considered. In the classification phase of the work, the well-known machine learning method Support Vector Machines (SVM) is used.…”

Section: Introductionmentioning

confidence: 99%

Local feature descriptors based ECG beat classification

Abdullah

Akpınar

Şengür

2020

Health Inf Sci Syst

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ECG beat type analysis is important in the detection of various heart diseases. The ECG beats give useful information about the status of the monitored heart condition. Up to now, various artificial intelligence-based methods have been proposed for ECG based heart failure detection. These methods were generally based on either time or frequency domain signal processing routines. In this study, we propose a different approach for ECG beat classification. The proposed approach is based on image processing. Thus, the initial step of the proposed work is converting the ECG beat signals to the ECG beat images. To do that, the ECG beat snapshots are initially saved as ECG beat images and then local feature descriptors are considered for feature extraction from ECG beat images. Eight local feature descriptors namely Local Binary Patterns, Frequency Decoded LBP, Quaternionic Local Ranking Binary Pattern, Binary Gabor Pattern, Local Phase Quantization, Binarized Statistical Image Features, CENsus TRansform hISTogram and Pyramid Histogram of Oriented Gradients are considered for feature extraction. The Support Vector Machines (SVM) classifier is used in the classification stage of the study. Linear, Quadratic, Cubic and Gaussian kernel functions are used in the SVM classifier. Five types of ECG beats from the MIT-BIH arrhythmia dataset are considered in experiments and the classification accuracy is used for performance measure. To construct a balanced training and test sets, 5000 and 10,000 ECG beat samples are randomly selected and are used in experiments in tenfold cross-validation fashion. The obtained resultsshow that the proposed method is quite efficient where the calculated accuracy score is 99.9% and the comparisons with the state-of-the-art method show that the proposed method outperforms other methods.

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Face retrieval using frequency decoded local descriptor

Cited by 28 publications

References 56 publications

Deep learning approaches for COVID-19 detection based on chest X-ray images

Deep learning approaches for COVID-19 detection based on chest X-ray images

Comparative Analysis of Texture Patterns on Mammograms for Classification

Local feature descriptors based ECG beat classification

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