loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Hamidreza Odabai Fard 1 ; Mohamed Chaouch 2 ; Quoc-cuong Pham 2 ; Antoine Vacavant 3 and Thierry Chateau 4

Affiliations: 1 CEA, LIST and Blaise Pascal University, France ; 2 CEA and LIST, France ; 3 University of Auvergne, France ; 4 Blaise Pascal University, France

ISBN: 978-989-758-004-8

Keyword(s): Multi-class Object Detection, Structured Support Vector Machines, Joint Learning.

Abstract: In practice, multiple objects in images are located by consecutively applying one detector for each class and taking the best confident score. In this work, we propose to show the advantage of grouping similar object classes into a hierarchical structure. While this approach has found interest in image classification, it is not analyzed for the object detection task. Each node in the hierarchy represents one decision line. All the decision lines are learned jointly using a novel problem formulation. Based on experiments using PASCAL VOC 2007 dataset, we show that our approach improves detection performance compared to a baseline approach.

PDF ImageFull Text

Download
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 34.236.170.48

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:
Odabai Fard, H.; Chaouch, M.; Pham, Q.; Vacavant, A. and Chateau, T. (2014). Joint Learning for Multi-class Object Detection.In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 104-112. DOI: 10.5220/0004692401040112

@conference{visapp14,
author={Hamidreza Odabai Fard. and Mohamed Chaouch. and Quoc{-}cuong Pham. and Antoine Vacavant. and Thierry Chateau.},
title={Joint Learning for Multi-class Object Detection},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={104-112},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004692401040112},
isbn={978-989-758-004-8},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - Joint Learning for Multi-class Object Detection
SN - 978-989-758-004-8
AU - Odabai Fard, H.
AU - Chaouch, M.
AU - Pham, Q.
AU - Vacavant, A.
AU - Chateau, T.
PY - 2014
SP - 104
EP - 112
DO - 10.5220/0004692401040112

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.