loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Marie Dumont ; Raphaël Marée ; Louis Wehenkel and Pierre Geurts

Affiliation: University of Liège, Belgium

ISBN: 978-989-8111-69-2

ISSN: 2184-4321

Keyword(s): Image annotation, Machine learning, Decision trees, Extremely randomized trees, Structured outputs.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computer Vision, Visualization and Computer Graphics ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Image and Video Analysis ; Segmentation and Grouping ; Sensor Networks ; Signal Processing ; Soft Computing ; Statistical Approach

Abstract: This paper addresses image annotation, i.e. labelling pixels of an image with a class among a finite set of predefined classes. We propose a new method which extracts a sample of subwindows from a set of annotated images in order to train a subwindow annotation model by using the extremely randomized trees ensemble method appropriately extended to handle high-dimensional output spaces. The annotation of a pixel of an unseen image is done by aggregating the annotations of its subwindows containing this pixel. The proposed method is compared to a more basic approach predicting the class of a pixel from a single window centered on that pixel and to other state-of-the-art image annotation methods. In terms of accuracy, the proposed method significantly outperforms the basic method and shows good performances with respect to the state-of-the-art, while being more generic, conceptually simpler, and of higher computational efficiency than these latter.

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 3.236.112.98

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:
Dumont M.; Marée R.; Wehenkel L.; Geurts P. and (2009). FAST MULTI-CLASS IMAGE ANNOTATION WITH RANDOM SUBWINDOWS AND MULTIPLE OUTPUT RANDOMIZED TREES.In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 196-203. DOI: 10.5220/0001800001960203

@conference{visapp09,
author={Marie Dumont and Raphaël Marée and Louis Wehenkel and Pierre Geurts},
title={FAST MULTI-CLASS IMAGE ANNOTATION WITH RANDOM SUBWINDOWS AND MULTIPLE OUTPUT RANDOMIZED TREES},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={196-203},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001800001960203},
isbn={978-989-8111-69-2},
}

TY - CONF

JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)
TI - FAST MULTI-CLASS IMAGE ANNOTATION WITH RANDOM SUBWINDOWS AND MULTIPLE OUTPUT RANDOMIZED TREES
SN - 978-989-8111-69-2
AU - Dumont, M.
AU - Marée, R.
AU - Wehenkel, L.
AU - Geurts, P.
PY - 2009
SP - 196
EP - 203
DO - 10.5220/0001800001960203

Login or register to post comments.

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