Large-scale nonlinear facial image classification based on approximate kernel Extreme Learning Machine

Alexandros Iosifidis; Anastasios Tefas; Ioannis Pitas

doi:10.1109/ICIP.2015.7351242

Large-scale nonlinear facial image classification based on approximate kernel Extreme Learning Machine

Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

Department of Electrical & Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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Abstract

In this paper, we propose a scheme that can be used in largescale nonlinear facial image classification problems. An approximate solution of the kernel Extreme Learning Machine classifier is formulated and evaluated. Experiments on two
publicly available facial image datasets using two popular facial image representations illustrate the effectiveness and efficiency of the proposed approach. The proposed Approximate Kernel Extreme Learning Machine classifier is able to scale well in both time and memory, while achieving good generalization performance. Specifically, it is shown that it outperforms the standard ELM approach for the same time and memory requirements. Compared to the original kernel ELM approach, it achieves similar (or better) performance,
while scaling well in both time and memory with respect to the training set cardinality.

Original language	English
Title of host publication	2015 IEEE International Conference on Image Processing (ICIP 2015)
Subtitle of host publication	Proceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	2449-2453
Number of pages	5
ISBN (Electronic)	9781479983391
ISBN (Print)	9781479983407
DOIs	https://doi.org/10.1109/ICIP.2015.7351242
Publication status	Published - Jan 2016
Event	2015 IEEE International Conference on Image Processing (ICIP) - Quebec City, ON, Canada Duration: 27 Sep 2015 → 30 Sep 2015

Conference

Conference	2015 IEEE International Conference on Image Processing (ICIP)
Country/Territory	Canada
City	Quebec City, ON
Period	27/09/15 → 30/09/15

Keywords

Nonlinear Facial Image Classification
Extreme Learning Machine
Approximate Methods

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10.1109/ICIP.2015.7351242

Ioannis Pitas - Large-scale nonlinear facial image classification based on approximate kernel extreme learning machine -2015Accepted author manuscript, 258 KB

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Iosifidis, A., Tefas, A., & Pitas, I. (2016). Large-scale nonlinear facial image classification based on approximate kernel Extreme Learning Machine. In 2015 IEEE International Conference on Image Processing (ICIP 2015): Proceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada (pp. 2449-2453 ). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICIP.2015.7351242

Iosifidis, Alexandros ; Tefas, Anastasios ; Pitas, Ioannis. / Large-scale nonlinear facial image classification based on approximate kernel Extreme Learning Machine. 2015 IEEE International Conference on Image Processing (ICIP 2015): Proceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada. Institute of Electrical and Electronics Engineers (IEEE), 2016. pp. 2449-2453

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abstract = "In this paper, we propose a scheme that can be used in largescale nonlinear facial image classification problems. An approximate solution of the kernel Extreme Learning Machine classifier is formulated and evaluated. Experiments on twopublicly available facial image datasets using two popular facial image representations illustrate the effectiveness and efficiency of the proposed approach. The proposed Approximate Kernel Extreme Learning Machine classifier is able to scale well in both time and memory, while achieving good generalization performance. Specifically, it is shown that it outperforms the standard ELM approach for the same time and memory requirements. Compared to the original kernel ELM approach, it achieves similar (or better) performance,while scaling well in both time and memory with respect to the training set cardinality.",

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Iosifidis, A, Tefas, A & Pitas, I 2016, Large-scale nonlinear facial image classification based on approximate kernel Extreme Learning Machine. in 2015 IEEE International Conference on Image Processing (ICIP 2015): Proceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada. Institute of Electrical and Electronics Engineers (IEEE), pp. 2449-2453 , 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, ON, Canada, 27/09/15. https://doi.org/10.1109/ICIP.2015.7351242

Large-scale nonlinear facial image classification based on approximate kernel Extreme Learning Machine. / Iosifidis, Alexandros; Tefas, Anastasios; Pitas, Ioannis.

2015 IEEE International Conference on Image Processing (ICIP 2015): Proceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada. Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 2449-2453 .

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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N2 - In this paper, we propose a scheme that can be used in largescale nonlinear facial image classification problems. An approximate solution of the kernel Extreme Learning Machine classifier is formulated and evaluated. Experiments on twopublicly available facial image datasets using two popular facial image representations illustrate the effectiveness and efficiency of the proposed approach. The proposed Approximate Kernel Extreme Learning Machine classifier is able to scale well in both time and memory, while achieving good generalization performance. Specifically, it is shown that it outperforms the standard ELM approach for the same time and memory requirements. Compared to the original kernel ELM approach, it achieves similar (or better) performance,while scaling well in both time and memory with respect to the training set cardinality.

AB - In this paper, we propose a scheme that can be used in largescale nonlinear facial image classification problems. An approximate solution of the kernel Extreme Learning Machine classifier is formulated and evaluated. Experiments on twopublicly available facial image datasets using two popular facial image representations illustrate the effectiveness and efficiency of the proposed approach. The proposed Approximate Kernel Extreme Learning Machine classifier is able to scale well in both time and memory, while achieving good generalization performance. Specifically, it is shown that it outperforms the standard ELM approach for the same time and memory requirements. Compared to the original kernel ELM approach, it achieves similar (or better) performance,while scaling well in both time and memory with respect to the training set cardinality.

KW - Nonlinear Facial Image Classification

KW - Extreme Learning Machine

KW - Approximate Methods

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DO - 10.1109/ICIP.2015.7351242

M3 - Conference Contribution (Conference Proceeding)

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T2 - 2015 IEEE International Conference on Image Processing (ICIP)

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Iosifidis A, Tefas A, Pitas I. Large-scale nonlinear facial image classification based on approximate kernel Extreme Learning Machine. In 2015 IEEE International Conference on Image Processing (ICIP 2015): Proceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada. Institute of Electrical and Electronics Engineers (IEEE). 2016. p. 2449-2453 Epub 2015 Sep 27. doi: 10.1109/ICIP.2015.7351242

Large-scale nonlinear facial image classification based on approximate kernel Extreme Learning Machine

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