Active classifier selection for RGB-D object categorization using a Markov random field ensemble method

Maximilian Durner; Zoltán Márton; Ulrich Hillenbrand; Haider Ali; Martin Kleinsteuber

doi:10.1117/12.2268551

17 March 2017 Active classifier selection for RGB-D object categorization using a Markov random field ensemble method

Maximilian Durner, Zoltán Márton, Ulrich Hillenbrand, Haider Ali, Martin Kleinsteuber

Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 103411I (2017) https://doi.org/10.1117/12.2268551
Event: Ninth International Conference on Machine Vision, 2016, Nice, France

Abstract

In this work, a new ensemble method for the task of category recognition in different environments is presented. The focus is on service robotic perception in an open environment, where the robot’s task is to recognize previously unseen objects of predefined categories, based on training on a public dataset. We propose an ensemble learning approach to be able to flexibly combine complementary sources of information (different state-of-the-art descriptors computed on color and depth images), based on a Markov Random Field (MRF). By exploiting its specific characteristics, the MRF ensemble method can also be executed as a Dynamic Classifier Selection (DCS) system. In the experiments, the committee- and topology-dependent performance boost of our ensemble is shown. Despite reduced computational costs and using less information, our strategy performs on the same level as common ensemble approaches. Finally, the impact of large differences between datasets is analyzed.

Citation Download Citation

Maximilian Durner, Zoltán Márton, Ulrich Hillenbrand, Haider Ali, and Martin Kleinsteuber "Active classifier selection for RGB-D object categorization using a Markov random field ensemble method", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103411I (17 March 2017); https://doi.org/10.1117/12.2268551

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