Improving a deep learning based RGB-D object recognition model by ensemble learning

Andreas Aakerberg, Kamal Nasrollahi, Thomas Heder

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 improve 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.

External IDs:dblp:conf/ipta/AakerbergNH17