LBP<sup>riu2</sup> Features for Classification of Radiographic Weld Images

Kumar, Jayendra; Arvind, Pratul; Singh, Prashant; Sarada, Yamini; Kumar, Neeraj; Bhardwaj, Shivain

doi:10.1109/icitaet47105.2019.9170146

Cited by 2 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using LBP features, both kNN and MLP classifiers obtained high accuracy since these methods can detect images with scaling and rotating invariance. The LBP features also represent the rotation-invariant of dataset [20], [44], [45]. [53].…”

Section: ) Conventional Feature Extraction Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Jaruenpunyasak

Duangsoithong

2021

IEEE Access

View full text Add to dashboard Cite

Deep learning algorithms are employed in many applications, especially in medical fields such as gait analysis and human pose detection for rehabilitation. However, creating the desired model with deep learning algorithms requires high memory and computing costs, which is problematic because deep learning technologies must be run on low-power devices such as edge computing equipment. To deal with these problems, feature reduction methods reduce the memory and energy costs. This paper presents an empirical analysis of deep learning with feature reduction. The method classifies foot images for knee rehabilitation using convolutional and dense autoencoders. The obtained results are compared with those of conventional methods (histograms of oriented gradients and local binary pattern algorithms). The features were classified and compared using support vector machine, k-nearest neighbor, and multilayer perceptron methods. The experimental results demonstrate that the conventional method uses fewer features than the deep learning method with higher accuracy because its algorithm projects pixels onto the histogram. In addition, using fewer features in deep learning layers maintains high accuracy, which is beneficial for edge computing implementations.

show abstract

Section: ) Conventional Feature Extraction Methodsmentioning

confidence: 99%

“…In the uniform LBP [42], [43], there are 59 features of the image. However, in our experiments, we needed to minimize the number of features, so the uniform rotation-invariant of LBP [20], [44], [45] was used for LBP feature extraction, where the R value varies from 1 to 4, and the P equals R multiplied by 8.…”

Section: ) Histogram Of Oriented Gradientsmentioning

confidence: 99%

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Jaruenpunyasak

Duangsoithong

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

Introspecting Local Binary Feature Vectors for Classification of Radiographic Weld Images

Kumar

Arvind²,

Singh³

et al. 2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

LBP^riu2 Features for Classification of Radiographic Weld Images

Cited by 2 publications

References 18 publications

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Introspecting Local Binary Feature Vectors for Classification of Radiographic Weld Images

Contact Info

Product

Resources

About

LBPriu2 Features for Classification of Radiographic Weld Images

Cited by 2 publications

References 18 publications

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Introspecting Local Binary Feature Vectors for Classification of Radiographic Weld Images

Contact Info

Product

Resources

About

LBP^riu2 Features for Classification of Radiographic Weld Images