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Ensemble feature learning for material recognition with convolutional neural networks

Peng Bian, Wanwan Li, Yi Jin, Ruicong Zhi
2018 EURASIP Journal on Image and Video Processing  
This paper proposes a novel approach named ensemble learning for material recognition with convolutional neural networks (CNNs).  ...  Finally, we propose three different ways to learn the ensemble features, which achieves higher recognition accuracy.  ...  Funding This work was supported in part by a grant from the National Natural Science Availability of data and materials We can provide the data.  ... 
doi:10.1186/s13640-018-0300-z fatcat:si3wvpmjonakneqi75cd5zj77a

Comprehensive approach for solving multimodal data analysis problems based on integration of evolutionary, neural and deep neural network algorithms

I Ivanov, E Sopov, I Panfilov
2018 IOP Conference Series: Materials Science and Engineering  
This approach involves multimodal data fusion techniques, multi-objective approach to feature selection and neural network ensemble optimization, as well as convolutional neural networks trained with hybrid  ...  The best emotion recognition accuracy achieved with the proposed approach on visual markers data is 65.8%, on audio features data -52.3%, on audio-visual data -71%.  ...  Acknowledgements The research is performed with the financial support of the Ministry of Education and Science of the Russian Federation within the State Assignment for the Siberian State Aerospace University  ... 
doi:10.1088/1757-899x/450/5/052007 fatcat:uaqlkkxwovfh3nfxa4w24dgy6i

Convolutional Neural Network Based on Extreme Learning Machine for Maritime Ships Recognition in Infrared Images

Atmane Khellal, Hongbin Ma, Qing Fei
2018 Sensors  
The success of Deep Learning models, notably convolutional neural networks (CNNs), makes them the favorable solution for object recognition systems in both visible and infrared domains.  ...  For instance, the proposed model is up to 950 times faster than the traditional back-propagation based training of convolutional neural networks, primarily for low-level features extraction.  ...  machine for maritime ships recognition in the infrared spectrum. (2) A fast unsupervised learning algorithm to train any convolutional neural network for features extraction is introduced. (3) An efficient  ... 
doi:10.3390/s18051490 pmid:29747439 pmcid:PMC5982679 fatcat:j22dpcc6gvafpghsj2a4y4dqem

Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks [article]

Henrique Siqueira, Sven Magg, Stefan Wermter
2020 arXiv   pre-print
In this paper, we present experiments on Ensembles with Shared Representations (ESRs) based on convolutional networks to demonstrate, quantitatively and qualitatively, their data processing efficiency  ...  In the context of deep learning, however, training an ensemble of deep networks is costly and generates high redundancy which is inefficient.  ...  Thomas Hellström for his insightful questions that motivated the development of this paper.  ... 
arXiv:2001.06338v1 fatcat:5hxzd5k4lnc3hllolhsbqnkuz4

Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks

Henrique Siqueira, Sven Magg, Stefan Wermter
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In this paper, we present experiments on Ensembles with Shared Representations (ESRs) based on convolutional networks to demonstrate, quantitatively and qualitatively, their data processing efficiency  ...  In the context of deep learning, however, training an ensemble of deep networks is costly and generates high redundancy which is inefficient.  ...  Thomas Hellström for his insightful questions that motivated the development of this paper.  ... 
doi:10.1609/aaai.v34i04.6037 fatcat:5vksys3drbakbokdlgqtjcicsa

Recognition efficiency enhancement of control chart pattern using ensemble MLP neural network

Sapna Kadakadiyavar, Achyutha N. Prasad, Piyush Kumar Pareek, V. Vani, V. S. Rekha, G. Nirmala
2022 International Journal of Health Sciences  
chart pattern for six different fundamental patterns have been proposed. The multilayer perceptron based neural network has applied as an entity to form the ensemble.  ...  The loss of generalization property of an individual neural network has been fulfilled by providing the diversity in the training data for the individual entity of ensemble.  ...  A method of control pattern recognition based on convolution neural network is proposed in [6][10] .CCPR method based on a one-dimensional convolutional neural network (1D-CNN) has proposed in [7] .  ... 
doi:10.53730/ijhs.v6ns3.6851 fatcat:d3dzdtglfjeaxfit2kwcpxgkni

Deep Neural Network Ensembles for Time Series Classification [article]

Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Muller
2019 arXiv   pre-print
We attribute this gap in performance due to the lack of neural network ensembles for TSC.  ...  Therefore in this paper, we show how an ensemble of 60 deep learning models can significantly improve upon the current state-of-the-art performance of neural networks for TSC, when evaluated over the UCR  ...  ACKNOWLEDGMENT The authors would like to thank the providers of the UCR/UEA benchmark datasets, as well as NVIDIA Corporation for the GPU Grant and the Mésocentre of Strasbourg for providing access to  ... 
arXiv:1903.06602v2 fatcat:do52wfihdbdfhhcgoxiltlfanu

Face Recognition on Low Resolution Face Image With TBE-CNN Architecture

Suharjito Suharjito, Atria Dika Puspita
2020 Advances in Science, Technology and Engineering Systems  
In this research will use the Trunk Branch Ensemble -Convolutional Neural Network (TBE-CNN) as one of Convolutional Neural Network (CNN) architecture combined with the pre-processing method which is super  ...  Face recognition in low resolution images has challenges in active research because face recognition is usually implemented in high resolution images (HR) .  ...  One of the deep learning algorithms that can be used to face recognition is Convolutional Neural Network (CNN). CNN is a type of neural network that is used for image classification.  ... 
doi:10.25046/aj050291 fatcat:5hnqfxfgzfhnjcsy2j3arpigga

SFE-Net: EEG-based Emotion Recognition with Symmetrical Spatial Feature Extraction [article]

Xiangwen Deng, Junlin Zhu, Shangming Yang
2021 arXiv   pre-print
In this paper, a spatial folding ensemble network (SFE-Net) is presented for EEG feature extraction and emotion recognition.  ...  Finally, a 3DCNN-based spatial, temporal extraction, and a multi-voting strategy of ensemble learning are integrated to model a new neural network.  ...  Cont-CNN is a convolutional neural network with no pooling operation, which takes a constructed 3D EEG cube as input.  ... 
arXiv:2104.06308v5 fatcat:266jsy6efzaqtiquof5dqppi6q

Racial Identity-Aware Facial Expression Recognition Using Deep Convolutional Neural Networks

Muhammad Sohail, Ghulam Ali, Javed Rashid, Israr Ahmad, Sultan H. Almotiri, Mohammed A. AlGhamdi, Arfan A. Nagra, Khalid Masood
2021 Applied Sciences  
In this research, a joint deep learning approach called racial identity aware deep convolution neural network is developed to recognize the multicultural facial expressions.  ...  For the reliability of the proposed joint learning technique, extensive experiments were performed with racial identity features and without racial identity features.  ...  Convolution neural network (CNN) based models have been widely used for racial identity recognition and facial emotions recognition. Vo et al.  ... 
doi:10.3390/app12010088 fatcat:jx4yh4sgobbgnbup7nrx3qhdpm

Automatic Identification of Peanut-Leaf Diseases Based on Stack Ensemble

Haixia Qi, Yu Liang, Quanchen Ding, Jun Zou
2021 Applied Sciences  
After ensemble by logistic regression, the accuracy of residual network with 50 layers (ResNet50) was as high as 97.59%, and the F1 score of dense convolutional network with 121 layers (DenseNet121) was  ...  Deep-learning networks with deeper network layers like ResNet50 and DenseNet121 performed better in this experiment. This study can provide a reference for the identification of peanut-leaf diseases.  ...  et al. proposed a nine-layer deep convolutional neural network plantdisease-recognition model.  ... 
doi:10.3390/app11041950 fatcat:i6kykhcty5fwvlclgshuu4iiia

The impact of ensemble learning on surgical tools classification during laparoscopic cholecystectomy

Jaafar Jaafari, Samira Douzi, Khadija Douzi, Badr Hssina
2022 Journal of Big Data  
The results present an improvement of approximately 6.19% and a mean average precision of 97.84% when the ensemble learning method is applied.  ...  In addition, an ensemble learning method is proposed, combining the three CNNs, to solve the tool presence detection problem as a multi-label classification problem.  ...  A convolutional neural network (CNN) is used to perform the tool classification task by automatically learning visual features from laparoscopic videos.  ... 
doi:10.1186/s40537-022-00602-6 fatcat:ygmryzlh6nawrckm23bum3z5gu

An Ensemble Learning Method for Dialect Classification

Shuai Ye, Ruoyan Zhao, Xinru Fang
2019 IOP Conference Series: Materials Science and Engineering  
This paper proposes an ensemble learning method for dialect classification. Firstly, the low accuracy of dialect data sets is processed and amplified.  ...  Dialect Classification Task is the first step of the Multilingual Automatic Speech Recognition System.  ...  Acknowledgments Thanks very much for the funding and support of Henan Province's College Students Innovation and Entrepreneurship Training Plan (project number: S201810459069) and Zhengzhou University's  ... 
doi:10.1088/1757-899x/569/5/052064 fatcat:lw5xb5o3izb3jmhyosdgthd7zm

A New Transfer Learning Ensemble Model with New Training Methods for Gear Wear Particle Recognition

Chunhua Zhao, zhangwen Lin, Jinling Tan, Hengxing Hu, Qian Li, Yi Qin
2022 Shock and Vibration  
its feature expression ability is stronger than that of the other four models.  ...  Compared with the other four models' experimental results, the model superiority in wear particle identification and classification is verified.  ...  Acknowledgments e authors thank the National Natural Science Foundation of China (no. 51975324) and Hubei Key Laboratory Open Fund (nos. 2018KJX10 and 2018KJX03) for supporting this research.  ... 
doi:10.1155/2022/3696091 fatcat:z2ypgrvn7rax3kddvek7m2lqcu

Dermatologist-Level Classification of Skin Cancer Using Cascaded Ensembling of Convolutional Neural Network and Handcrafted Features Based Deep Neural Network

Akhilesh Kumar Sharma, Shamik Tiwari, Gaurav Aggarwal, Nitika Goenka, Anil Kumar, Prasun Chakrabarti, Tulika Chakrabarti, Radomir Gono, Zbigniew Leonowicz, Michal Jasinski
2022 IEEE Access  
It is demonstrated that accuracy of ensembled deep learning model is improved to 98.3% from 85.3% of convolutional neural network model.  ...  The deep learning architectures such as recurrent networks and convolutional neural networks (ConvNets) are developed in the past, which are proven appropriate for non-handcrafted extraction of complex  ...  This ensembled classifier has combined the Back Propagation Neural Network (BPNN) with Fuzzy Neural Network (FNN) to gain high accuracy.  ... 
doi:10.1109/access.2022.3149824 fatcat:hqlfjusvavdpfcqkqai55nwe2u
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