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

Publikation: Forskning - peer reviewKonferenceartikel i proceeding

Abstrakt

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.
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Detaljer

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.
OriginalsprogEngelsk
TitelInternational Conference on Image Processing Theory, Tools and Applications
ForlagIEEE
Publikationsdato1 okt. 2017
StatusAccepteret/In press - 1 okt. 2017
PublikationsartForskning
Peer reviewJa
BegivenhedInternational Conference on Image Processing Theory, Tools and Applications - Montreal, Canada
Varighed: 28 nov. 20171 dec. 2017

Konference

KonferenceInternational Conference on Image Processing Theory, Tools and Applications
LandCanada
ByMontreal
Periode28/11/201701/12/2017

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