@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 = mar,
day = "8",
doi = "10.1109/IPTA.2017.8310101",
language = "English",
isbn = "978-1-5386-1843-1",
series = "International Conference on Image Processing Theory, Tools and Applications (IPTA)",
pages = "1--6",
booktitle = "2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)",
publisher = "IEEE",
address = "United States",
note = "International Conference on Image Processing Theory, Tools and Applications, IPTA ; Conference date: 28-11-2017 Through 01-12-2017",
}