loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Author: Haider Ali

Affiliation: University of Leoben, Austria

Keyword(s): Object classification, Object hierarchies, ImageNet, Object detection and Domain Ontologies.

Abstract: We present a binary tree based object classification method in this paper. The binary tree builds a group of classes using ImageNet domain ontologies. A binary decision function is introduced in the root node of the decision tree using the positive samples of the first group for training. The decision function continues dividing the groups in sub-sequent groups when approaching the leaf nodes and provides positive and negative samples for multi-class problems. We have tested our method on the PASCAL Visual Object Classes Challenge 2006 (VOC2006) dataset and have achieved comparable accuracy for group classification. The results show that the proposed method is a powerful class binarization technique for hierarchical objects group classification.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.117.153.38

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Ali, H. (2010). HIERARCHICAL OBJECT CLASSIFICATION USING IMAGENET DOMAIN ONTOLOGIES. In Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2010) - Volume 2: VISAPP; ISBN 978-989-674-029-0; ISSN 2184-4321, SciTePress, pages 534-536. DOI: 10.5220/0002851905340536

@conference{visapp10,
author={Haider Ali.},
title={HIERARCHICAL OBJECT CLASSIFICATION USING IMAGENET DOMAIN ONTOLOGIES},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2010) - Volume 2: VISAPP},
year={2010},
pages={534-536},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002851905340536},
isbn={978-989-674-029-0},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2010) - Volume 2: VISAPP
TI - HIERARCHICAL OBJECT CLASSIFICATION USING IMAGENET DOMAIN ONTOLOGIES
SN - 978-989-674-029-0
IS - 2184-4321
AU - Ali, H.
PY - 2010
SP - 534
EP - 536
DO - 10.5220/0002851905340536
PB - SciTePress