Bi-directional extreme learning machine for semi-blind watermarking of compressed images

Mishra, Anurag; Rajpal, Ankit; Bala, Rajni

doi:10.1016/j.jisa.2017.11.008

Cited by 19 publications

(15 citation statements)

References 23 publications

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“…The bias and the weights of every hidden layer and input layer are randomly produced, respectively. A. Mishra, A. Rajpal, and R. Bala, [18] introduced the Bi-directional ELM (B-ELM) for obtaining the watermarking of the JPEG images and this B-ELM has the capacity of fast training with less number of hidden neurons. The principle behind the B-ELM is to optimize the two parameters which is need in the hidden layer represented as ( , ).…”

Section: Literature Surveymentioning

confidence: 99%

Automotive Rank Based ELM Using Iterative Decomposition

Nagelli¹,

Ragala²,

B³

2019

IJIES

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Nowadays, the Extreme learning machine (ELM) is playing a key role in machine intelligence and big data analytics due to its various advantages such as fast training rate, universal classification/regression and the capability of approximation. The standard ELM uses the Moore-Penrose generalized pseudo-inverse for solving the hidden layer activation matrix and also it identifies the output weights. Because of that, the standard ELM takes more time to train the features from the dataset. In ELM, scalability also considered as a one of the major concern while processing the large dataset. In order to overcome this concern, the Automotive Rank based ELM (AR-ELM) is proposed to obtain an effective tensor decomposition for diminishing the training time. Besides, the Bayesian approach is considered in this AR-ELM to remove the redundancy from the decomposed samples of the tensor. The major objective of this proposed AR-ELM is to process the large amount of dataset without depending on memory capacities. The recognition accuracy is improved by eliminating redundant information. The key idea of the AR-ELM is to reduce the training time while processing the huge dataset. The implementation and simulation of the AR-ELM is done in Spark Python 3.7. The performance of the AR-ELM is analysed in terms of accuracy, precision, recall and training time. The proposed methodology is compared with three existing methodologies such as basic ELM, ELM-TUCKER and ELM-PARAFAC. The recognition accuracy of the AR-ELM methodology with Hardlims activation function is 0.8879 for letter recognition dataset, it is high when compared to the basic ELM, ELM-TUCKER and ELM-PARAFAC that are 0.8102, 0.8375 and 0.834 respectively.

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Section: Literature Surveymentioning

confidence: 99%

Automotive Rank Based ELM Using Iterative Decomposition

Nagelli¹,

Ragala²,

B³

2019

IJIES

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“…ELM is a kind of single-hidden layer feed-forward neural networks (SLFNs), where the input weights and biases are randomly generated and never updated. Due to its excellent advantage of faster learning speed and higher generalization performance, ELM has been widely used to solve the regression and classification problem (Mishra et al , 2018; Deng et al , 2017; Singh et al , 2016; Huang et al , 2012). The structure of SLFNs can be illustrated by Figure 7.…”

Section: Extreme Learning Machine-based Image Compressionmentioning

confidence: 99%

A fast ELM-based machine compression scheme for underwater image transmission on a low-bandwidth acoustic channel

Zhang

Cui

et al. 2019

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Purpose This paper aims to propose a fast machine compression scheme, which can solve the problem of low-bandwidth transmission for underwater images. Design/methodology/approach This fast machine compression scheme mainly consists of three stages. Firstly, raw images are fed into the image pre-processing module, which is specially designed for underwater color images. Secondly, a divide-and-conquer (D&C) image compression framework is developed to divide the problem of image compression into a manageable size. And extreme learning machine (ELM) is introduced to substitute for principal component analysis (PCA), which is a traditional transform-based lossy compression algorithm. The execution time of ELM is very short, thus the authors can compress the images at a much faster speed. Finally, underwater color images can be recovered from the compressed images. Findings Experiment results show that the proposed scheme can not only compress the images at a much faster speed but also maintain the acceptable perceptual quality of reconstructed images. Originality/value This paper proposes a fast machine compression scheme, which combines the traditional PCA compression algorithm with the ELM algorithm. Moreover, a pre-processing module and a D&C image compression framework are specially designed for underwater images.

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“…The ELM is a batch learning algorithm proposed by Huang et al [19] and has been used extensively in different domains like ECG signal classification [21] and identification of arrhythmia disease [22]. The ELM and its variants have also been applied in applications such as fingerprint identification [23], lung cancer detection [24], image and video watermarking [25,26], and 3D object recognition [27]. The applicability of ELM in various domains is due to its fast learning, good generalization performance, and ease of implementation.…”

Section: Introductionmentioning

confidence: 99%

COV-ELM classifier: An Extreme Learning Machine based identification of COVID-19 using Chest X-Ray Images

Rajpal¹,

Agarwal²,

Rajpal³

et al. 2020

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Coronaviruses constitute a family of virus that gives rise to respiratory diseases. Coronavirus disease 2019 is an infectious disease caused by a newly discovered coronavirus also termed as Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to its rapid spread, WHO has declared COVID-19 outbreak a pandemic on 11th March 2020. Reverse transcription-polymerase chain reaction (RT-PCR) test is popularly used worldwide for the detection of COVID-19. However, due to the high false-negative rate of RT-PCR test, chest X-ray (CXR) imaging is emerging as a feasible alternative for the detection of COVID-19. In this work, we propose a multiclass classification model COV-ELM, based on the extreme learning machine which classifies the CXR images into one of the three classes, namely COVID-19, normal, and pneumonia. The choice of ELM in this work has been motivated by its significantly short training time as compared to conventional gradient-based learning algorithms. After some preprocessing, we extract a pool of features based on texture and frequency. This pool of features serves as an input to the ELM and a 10-fold cross-validation method is employed to evaluate the proposed model. For experimentation, we use chest X-ray (CXR) images from three publicly available sources. The results of applying COV-ELM on test data are quite promising. The COV-ELM achieved a macro average F1-score of 0.95 and the overall sensitivity of 0.94 ± 0.02 at 95% confidence interval. When compared to state-of-the-art machine learning algorithms, the COV-ELM is found to outperform in a three-class classification scenario. The main advantage of COV-ELM is that its training time being quite low, as bigger and diverse datasets become available, it can be quickly retrained as compared to its gradient-based competitor models.

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Bi-directional extreme learning machine for semi-blind watermarking of compressed images

Cited by 19 publications

References 23 publications

Automotive Rank Based ELM Using Iterative Decomposition

Automotive Rank Based ELM Using Iterative Decomposition

A fast ELM-based machine compression scheme for underwater image transmission on a low-bandwidth acoustic channel

COV-ELM classifier: An Extreme Learning Machine based identification of COVID-19 using Chest X-Ray Images

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