Improving a Deep Learning based RGB-D Object Recognition Model by Ensemble Learning

Andreas Aakerberg, Kamal Nasrollahi, Thomas Heder

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

3 Citations (Scopus)
299 Downloads (Pure)

Abstract

Augmenting RGB images with depth information is a well-known method to significantly improve the recognition accuracy of object recognition models. Another method to im- prove the performance of visual recognition models is ensemble learning. However, this method has not been widely explored in combination with deep convolutional neural network based RGB-D object recognition models. Hence, in this paper, we form different ensembles of complementary deep convolutional neural network models, and show that this can be used to increase the recognition performance beyond existing limits. Experiments on the Washington RGB-D Object Dataset show that our best performing ensemble improves the recognition performance with 0.7% compared to using the baseline model alone.
Original languageEnglish
Title of host publication2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)
Number of pages6
PublisherIEEE
Publication date8 Mar 2018
Pages1-6
Article number8310101
ISBN (Print)978-1-5386-1843-1
ISBN (Electronic)978-1-5386-1842-4
DOIs
Publication statusPublished - 8 Mar 2018
EventInternational Conference on Image Processing Theory, Tools and Applications - Montreal, Canada
Duration: 28 Nov 20171 Dec 2017

Conference

ConferenceInternational Conference on Image Processing Theory, Tools and Applications
CountryCanada
CityMontreal
Period28/11/201701/12/2017
SeriesInternational Conference on Image Processing Theory, Tools and Applications (IPTA)
ISSN2154-512X

Fingerprint

Object recognition
Neural networks
Deep learning
Experiments

Keywords

  • Computer Vision
  • Convolutional Neural Networks
  • Deep Learning
  • Ensemble Learning
  • RGB-D

Cite this

Aakerberg, A., Nasrollahi, K., & Heder, T. (2018). Improving a Deep Learning based RGB-D Object Recognition Model by Ensemble Learning. In 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) (pp. 1-6). [8310101] IEEE. International Conference on Image Processing Theory, Tools and Applications (IPTA) https://doi.org/10.1109/IPTA.2017.8310101
Aakerberg, Andreas ; Nasrollahi, Kamal ; Heder, Thomas. / Improving a Deep Learning based RGB-D Object Recognition Model by Ensemble Learning. 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2018. pp. 1-6 (International Conference on Image Processing Theory, Tools and Applications (IPTA)).
@inproceedings{59ad60be27db438a8f2e0ea40207070c,
title = "Improving a Deep Learning based RGB-D Object Recognition Model by Ensemble Learning",
abstract = "Augmenting RGB images with depth information is a well-known method to significantly improve the recognition accuracy of object recognition models. Another method to im- prove the performance of visual recognition models is ensemble learning. However, this method has not been widely explored in combination with deep convolutional neural network based RGB-D object recognition models. Hence, in this paper, we form different ensembles of complementary deep convolutional neural network models, and show that this can be used to increase the recognition performance beyond existing limits. Experiments on the Washington RGB-D Object Dataset show that our best performing ensemble improves the recognition performance with 0.7{\%} compared to using the baseline model alone.",
keywords = "Computer Vision, Convolutional Neural Networks, Deep Learning, Ensemble Learning, RGB-D",
author = "Andreas Aakerberg and Kamal Nasrollahi and Thomas Heder",
year = "2018",
month = "3",
day = "8",
doi = "10.1109/IPTA.2017.8310101",
language = "English",
isbn = "978-1-5386-1843-1",
pages = "1--6",
booktitle = "2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)",
publisher = "IEEE",
address = "United States",

}

Aakerberg, A, Nasrollahi, K & Heder, T 2018, Improving a Deep Learning based RGB-D Object Recognition Model by Ensemble Learning. in 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)., 8310101, IEEE, International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1-6, International Conference on Image Processing Theory, Tools and Applications, Montreal, Canada, 28/11/2017. https://doi.org/10.1109/IPTA.2017.8310101

Improving a Deep Learning based RGB-D Object Recognition Model by Ensemble Learning. / Aakerberg, Andreas; Nasrollahi, Kamal; Heder, Thomas.

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2018. p. 1-6 8310101 (International Conference on Image Processing Theory, Tools and Applications (IPTA)).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

TY - GEN

T1 - Improving a Deep Learning based RGB-D Object Recognition Model by Ensemble Learning

AU - Aakerberg, Andreas

AU - Nasrollahi, Kamal

AU - Heder, Thomas

PY - 2018/3/8

Y1 - 2018/3/8

N2 - Augmenting RGB images with depth information is a well-known method to significantly improve the recognition accuracy of object recognition models. Another method to im- prove the performance of visual recognition models is ensemble learning. However, this method has not been widely explored in combination with deep convolutional neural network based RGB-D object recognition models. Hence, in this paper, we form different ensembles of complementary deep convolutional neural network models, and show that this can be used to increase the recognition performance beyond existing limits. Experiments on the Washington RGB-D Object Dataset show that our best performing ensemble improves the recognition performance with 0.7% compared to using the baseline model alone.

AB - Augmenting RGB images with depth information is a well-known method to significantly improve the recognition accuracy of object recognition models. Another method to im- prove the performance of visual recognition models is ensemble learning. However, this method has not been widely explored in combination with deep convolutional neural network based RGB-D object recognition models. Hence, in this paper, we form different ensembles of complementary deep convolutional neural network models, and show that this can be used to increase the recognition performance beyond existing limits. Experiments on the Washington RGB-D Object Dataset show that our best performing ensemble improves the recognition performance with 0.7% compared to using the baseline model alone.

KW - Computer Vision

KW - Convolutional Neural Networks

KW - Deep Learning

KW - Ensemble Learning

KW - RGB-D

UR - http://www.scopus.com/inward/record.url?scp=85050615657&partnerID=8YFLogxK

U2 - 10.1109/IPTA.2017.8310101

DO - 10.1109/IPTA.2017.8310101

M3 - Article in proceeding

SN - 978-1-5386-1843-1

SP - 1

EP - 6

BT - 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)

PB - IEEE

ER -

Aakerberg A, Nasrollahi K, Heder T. Improving a Deep Learning based RGB-D Object Recognition Model by Ensemble Learning. In 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE. 2018. p. 1-6. 8310101. (International Conference on Image Processing Theory, Tools and Applications (IPTA)). https://doi.org/10.1109/IPTA.2017.8310101