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.
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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.
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