NDLI: Localized Deep Extreme Learning Machines for Efficient RGB-D Object Recognition

Content Provider	IEEE Xplore Digital Library
Author	Zaki, H.F.M. Shafait, F. Mian, A.
Copyright Year	2015
Description	Author affiliation: Nat. Univ. of Sci. & Technol., Islamabad, Pakistan (Shafait, F.) \|\| Comput. Sci. & Software Eng., Univ. of Western Australia, Crawley, WA, Australia (Zaki, H.F.M.) \|\| Mechatron. Eng., Int. Islamic Univ. Malaysia, Kuala Lumpur, Malaysia (Mian, A.)
Abstract	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.
Starting Page	1
Ending Page	8
File Size	887778
Page Count	8
File Format	PDF
e-ISBN	9781467367950
DOI	10.1109/DICTA.2015.7371280
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-23
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Visualization Three-dimensional displays Neural networks Training data Feature extraction Object recognition
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Discriminative feature learning for efficient RGB-D object recognition

Convolutional neural network for 3D object recognition based on RGB-D dataset

Convolutional Fisher Kernels for RGB-D Object Recognition

Recognizing RGB Images by Learning from RGB-D Data

Hybrid RGB-D object recognition using Convolutional Neural Network and Fisher Vector

Accurate Localization of 3D Objects from RGB-D Data Using Segmentation Hypotheses

Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation

On RGB-D face recognition using Kinect

Deep feature representation for hyperspectral image classification

Localized Deep Extreme Learning Machines for Efficient RGB-D Object Recognition

Similar Documents

Discriminative feature learning for efficient RGB-D object recognition

Convolutional neural network for 3D object recognition based on RGB-D dataset

Convolutional Fisher Kernels for RGB-D Object Recognition

Recognizing RGB Images by Learning from RGB-D Data

Hybrid RGB-D object recognition using Convolutional Neural Network and Fisher Vector

Accurate Localization of 3D Objects from RGB-D Data Using Segmentation Hypotheses

Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation

On RGB-D face recognition using Kinect

Deep feature representation for hyperspectral image classification

Localized Deep Extreme Learning Machines for Efficient RGB-D Object Recognition