Localized deep extreme learning machines for efficient RGB-D object recognition

Existing RGB-D object recognition methods either use channel specific handcrafted features, or learn features with deep networks. The former lack representation ability while the latter require large amounts of training data and learning time. In real-time robotics applications involving RGB-D senso...

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Main Authors:	Mohd Zaki, Hasan Firdaus, Shafait, Faisal, Mian, Ajmal S.
Format:	Conference or Workshop Item
Language:	English English
Published:	Institute of Electrical and Electronic Engineers, Inc. (IEEE) 2015
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/64704/ http://irep.iium.edu.my/64704/ http://irep.iium.edu.my/64704/ http://irep.iium.edu.my/64704/7/64704%20Localized%20Deep%20Extreme%20Learning.pdf http://irep.iium.edu.my/64704/8/64704%20Localized%20Deep%20Extreme%20Learning%20SCOPUS.pdf

id	iium-64704
recordtype	eprints
spelling	iium-647042018-08-06T07:02:51Z http://irep.iium.edu.my/64704/ Localized deep extreme learning machines for efficient RGB-D object recognition Mohd Zaki, Hasan Firdaus Shafait, Faisal Mian, Ajmal S. QA75 Electronic computers. Computer science Existing RGB-D object recognition methods either use channel specific handcrafted features, or learn features with deep networks. The former lack representation ability while the latter require large amounts of training data and learning time. In real-time robotics applications involving RGB-D sensors, we do not have the luxury of both. In this paper, we propose Localized Deep Extreme Learning Machines (LDELM) that efficiently learn features from RGB-D data. By using localized patches, not only is the problem of data sparsity solved, but the learned features are robust to occlusions and viewpoint variations. LDELM learns deep localized features in an unsupervised way from random patches of the training data. Each image is then feed-forwarded, patch-wise, through the LDELM to form a cuboid of features. The cuboid is divided into cells and pooled to get the final compact image representation which is then used to train an ELM classifier. Experiments on the benchmark Washington RGB-D and 2D3D datasets show that the proposed algorithm not only is significantly faster to train but also outperforms state-of-the-art methods in terms of accuracy and classification time. Institute of Electrical and Electronic Engineers, Inc. (IEEE) 2015 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/64704/7/64704%20Localized%20Deep%20Extreme%20Learning.pdf application/pdf en http://irep.iium.edu.my/64704/8/64704%20Localized%20Deep%20Extreme%20Learning%20SCOPUS.pdf Mohd Zaki, Hasan Firdaus and Shafait, Faisal and Mian, Ajmal S. (2015) Localized deep extreme learning machines for efficient RGB-D object recognition. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 23rd-25th November 2015, Adelaide, SA, Australia. https://ieeexplore.ieee.org/document/7371280/ 10.1109/DICTA.2015.7371280
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Mohd Zaki, Hasan Firdaus Shafait, Faisal Mian, Ajmal S. Localized deep extreme learning machines for efficient RGB-D object recognition
description	Existing RGB-D object recognition methods either use channel specific handcrafted features, or learn features with deep networks. The former lack representation ability while the latter require large amounts of training data and learning time. In real-time robotics applications involving RGB-D sensors, we do not have the luxury of both. In this paper, we propose Localized Deep Extreme Learning Machines (LDELM) that efficiently learn features from RGB-D data. By using localized patches, not only is the problem of data sparsity solved, but the learned features are robust to occlusions and viewpoint variations. LDELM learns deep localized features in an unsupervised way from random patches of the training data. Each image is then feed-forwarded, patch-wise, through the LDELM to form a cuboid of features. The cuboid is divided into cells and pooled to get the final compact image representation which is then used to train an ELM classifier. Experiments on the benchmark Washington RGB-D and 2D3D datasets show that the proposed algorithm not only is significantly faster to train but also outperforms state-of-the-art methods in terms of accuracy and classification time.
format	Conference or Workshop Item
author	Mohd Zaki, Hasan Firdaus Shafait, Faisal Mian, Ajmal S.
author_facet	Mohd Zaki, Hasan Firdaus Shafait, Faisal Mian, Ajmal S.
author_sort	Mohd Zaki, Hasan Firdaus
title	Localized deep extreme learning machines for efficient RGB-D object recognition
title_short	Localized deep extreme learning machines for efficient RGB-D object recognition
title_full	Localized deep extreme learning machines for efficient RGB-D object recognition
title_fullStr	Localized deep extreme learning machines for efficient RGB-D object recognition
title_full_unstemmed	Localized deep extreme learning machines for efficient RGB-D object recognition
title_sort	localized deep extreme learning machines for efficient rgb-d object recognition
publisher	Institute of Electrical and Electronic Engineers, Inc. (IEEE)
publishDate	2015
url	http://irep.iium.edu.my/64704/ http://irep.iium.edu.my/64704/ http://irep.iium.edu.my/64704/ http://irep.iium.edu.my/64704/7/64704%20Localized%20Deep%20Extreme%20Learning.pdf http://irep.iium.edu.my/64704/8/64704%20Localized%20Deep%20Extreme%20Learning%20SCOPUS.pdf
first_indexed	2023-09-18T21:31:49Z
last_indexed	2023-09-18T21:31:49Z
_version_	1777412561377951744

Localized deep extreme learning machines for efficient RGB-D object recognition

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