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

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25 Citations (Scopus)

Abstract

© 2015 IEEE. 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.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Robotics and Automation (ICRA)
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1295-1302
Volume2015-June
ISBN (Print)10504729
DOIs
Publication statusPublished - 2015
Event2015 IEEE International Conference on Robotics and Automation (ICRA) - Seattle, United States
Duration: 26 May 201530 May 2015

Conference

Conference2015 IEEE International Conference on Robotics and Automation (ICRA)
Country/TerritoryUnited States
CitySeattle
Period26/05/1530/05/15

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