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Efficient RGB-D object categorization using cascaded ensembles of randomized decision trees

Asif, U., Bennamoun, M. and Sohel, F. (2015) Efficient RGB-D object categorization using cascaded ensembles of randomized decision trees. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), 26-30 May 2015, Seattle, Washington pp. 1295-1302.

Link to Published Version: http://dx.doi.org/10.1109/ICRA.2015.7139358
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Abstract

This paper presents an efficient framework for the categorization of objects in real-world scenes (captured with an RGB-D sensor). The proposed framework uses ensembles of randomized decision trees in a hierarchical cascaded architecture to compute consistent object-class inferences of unseen objects. Specifically, the proposed framework computes object-class probabilities at three levels of an image hierarchy (i.e., pixel-, surfel-, and object-levels) using Random Forest classifiers. Next, these probabilities are fused together to compute a cumulative probabilistic output which is used to infer object categories. This fusion results in an improved object categorization performance compared with the state-of-the-art methods.

Item Type: Conference Paper
URI: http://researchrepository.murdoch.edu.au/id/eprint/28454
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