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Author Asif, U. ♦ Bennamoun, M. ♦ Sohel, F.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2015
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Other branches of engineering
Subject Keyword Feature extraction ♦ Three-dimensional displays ♦ Training ♦ Histograms ♦ Image color analysis ♦ Probabilistic logic ♦ Decision trees
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.
Description Author affiliation: Sch. of Comput. Sci. & Software Eng., Univ. of Western Australia, Perth, WA, Australia (Asif, U.; Bennamoun, M.; Sohel, F.)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-05-26
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781479969234
Size (in Bytes) 3.60 MB
Page Count 8
Starting Page 1295
Ending Page 1302


Source: IEEE Xplore Digital Library