@inproceedings{d0e21dcb512e402e84d633a13c6029fd,
title = "Extracting Categories By Hierarchical Clustering Using Global Relational Features",
abstract = "We introduce an object categorization system which uses hierarchical clustering to extract categories. The system is able to assign multiple, nested categories for unseen objects. In our system, objects are represented with global pair-wise relations computed from 3D features extracted by three RGB-D sensors. We show that our system outperforms a state-of-the-art approach particularly when only a few number of training samples is used.",
author = "Wail Mustafa and Dirk Kraft and Norbert Kr{\"u}ger",
year = "2015",
doi = "10.1007/978-3-319-19390-8_61",
language = "English",
isbn = "978-3-319-19389-2",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "541--551",
editor = "Roberto Paredes and Cardoso, {Jaime S.} and Pardo, {Xos{\'e} M.}",
booktitle = "Pattern Recognition and Image Analysis",
address = "Germany",
note = "7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA ; Conference date: 17-07-2015 Through 19-07-2015",
}