NMF-Based Image Quality Assessment Using Extreme Learning Machine

Wang, Shuigen; Deng, Chenwei; Wang, Lin; Huang, Guang-Bin; Zhao, Baojun

doi:10.1109/tcyb.2015.2512852

Cited by 75 publications

(37 citation statements)

References 38 publications

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“…Huang et al proposed ELM in his pioneering paper in 2004 [29,31]. ELM is a special simple and single hidden layer neural network, whose advantages include simplicity, fast speed, and global optimization.…”

Section: Traffic Distribution Density and Its Quantificationmentioning

confidence: 99%

Distributed Routing Strategy Based on Machine Learning for LEO Satellite Network

Pan

Liu

et al. 2018

Wireless Communications and Mobile Computing

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As the indispensable supplement of terrestrial communications, Low Earth Orbit (LEO) satellite network is the crucial part in future space-terrestrial integrated networks because of its unique advantages. However, the effective and reliable routing for LEO satellite network is an intractable task due to time-varying topology, frequent link handover, and imbalanced communication load. An Extreme Learning Machine (ELM) based distributed routing (ELMDR) strategy was put forward in this paper. Considering the traffic distribution density on the surface of the earth, ELMDR strategy makes routing decision based on traffic prediction. For traffic prediction, ELM, which is a fast and efficient machine learning algorithm, is adopted to forecast the traffic at satellite node. For the routing decision, mobile agents (MAs) are introduced to simultaneously and independently search for LEO satellite network and determine routing information. Simulation results demonstrate that, in comparison to the conventional Ant Colony Optimization (ACO) algorithm, ELMDR not only sufficiently uses underutilized link, but also reduces delay.

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Section: Traffic Distribution Density and Its Quantificationmentioning

confidence: 99%

Distributed Routing Strategy Based on Machine Learning for LEO Satellite Network

Pan

Liu

et al. 2018

Wireless Communications and Mobile Computing

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“…In this section, we will introduce one new feature representation algorithm, ELM-AE, which is based on one very fast and effective neural network named Extreme Learning Machine (ELM) [19,20]. Just like the traditional ELM [21][22][23], ELM-AE contains three layers: input layer, hidden layer, and output layer.…”

Section: Elm-aementioning

confidence: 99%

Domain Adaption Based on ELM Autoencoder

Zhang

2017

Mathematical Problems in Engineering

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We propose a new ELM Autoencoder (ELM-AE) based domain adaption algorithm which describes the subspaces of source and target domain by ELM-AE and then carries out subspace alignment to project different domains into a common new space. By leveraging nonlinear approximation ability and efficient one-pass learning ability of ELM-AE, the proposed domain adaption algorithm can efficiently seek a better cross-domain feature representation than linear feature representation approaches such as PCA to improve domain adaption performance. The widely experimental results on Office/Caltech-256 datasets show that the proposed algorithm can achieve better classification accuracy than PCA subspace alignment algorithm and other state-of-the-art domain adaption algorithms in most cases.

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“…These algorithms have reached high-level performance, while in most possible situations, the reference messages are not easy or impossible to acquire. Thus, no-reference or blind IQA methods are more useful in real applications [23–34]. …”

Section: Introductionmentioning

confidence: 99%

A shallow convolutional neural network for blind image sharpness assessment

Wang

et al. 2017

PLoS ONE

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Blind image quality assessment can be modeled as feature extraction followed by score prediction. It necessitates considerable expertise and efforts to handcraft features for optimal representation of perceptual image quality. This paper addresses blind image sharpness assessment by using a shallow convolutional neural network (CNN). The network takes single feature layer to unearth intrinsic features for image sharpness representation and utilizes multilayer perceptron (MLP) to rate image quality. Different from traditional methods, CNN integrates feature extraction and score prediction into an optimization procedure and retrieves features automatically from raw images. Moreover, its prediction performance can be enhanced by replacing MLP with general regression neural network (GRNN) and support vector regression (SVR). Experiments on Gaussian blur images from LIVE-II, CSIQ, TID2008 and TID2013 demonstrate that CNN features with SVR achieves the best overall performance, indicating high correlation with human subjective judgment.

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NMF-Based Image Quality Assessment Using Extreme Learning Machine

Cited by 75 publications

References 38 publications

Distributed Routing Strategy Based on Machine Learning for LEO Satellite Network

Distributed Routing Strategy Based on Machine Learning for LEO Satellite Network

Domain Adaption Based on ELM Autoencoder

A shallow convolutional neural network for blind image sharpness assessment

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