LEARNING OBJECT SEGMENTATION USING A MULTI NETWORK SEGMENT CLASSIFICATION APPROACH

S. Albertini; I. Gallo; M. Vanetti; A. Nodari

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Paper

LEARNING OBJECT SEGMENTATION USING A MULTI NETWORK SEGMENT CLASSIFICATION APPROACH

Topics: Early and Biologically-Inspired Vision; Machine Learning Technologies for Vision; Object and Face Recognition; Segmentation and Grouping

In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, 521-530, 2012 , Rome, Italy

Authors: S. Albertini ; I. Gallo ; M. Vanetti and A. Nodari

Affiliation: University of Insubria, Italy

Keyword(s): Object Segmentation, Multi-net System, GrabCut.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Early and Biologically-Inspired Vision ; Image and Video Analysis ; Segmentation and Grouping

Abstract: In this study we propose a new strategy to perform an object segmentation using a multi neural network approach. We started extending our previously presented object detection method applying a new segment based classification strategy. The result obtained is a segmentation map post processed by a phase that exploits the GrabCut algorithm to obtain a fairly precise and sharp edges of the object of interest in a full automatic way. We tested the new strategy on a clothing commercial dataset obtaining a substantial improvement on the quality of the segmentation results compared with our previous method. The segment classification approach we propose achieves the same improvement on a subset of the Pascal VOC 2011 dataset which is a recent standard segmentation dataset, obtaining a result which is inline with the state of the art.

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Paper citation in several formats:

Albertini, S.; Gallo, I.; Vanetti, M. and Nodari, A. (2012). LEARNING OBJECT SEGMENTATION USING A MULTI NETWORK SEGMENT CLASSIFICATION APPROACH. In Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2012) - Volume 2: VISAPP; ISBN 978-989-8565-03-7; ISSN 2184-4321, SciTePress, pages 521-530. DOI: 10.5220/0003833705210530

@conference{visapp12,
author={S. Albertini. and I. Gallo. and M. Vanetti. and A. Nodari.},
title={LEARNING OBJECT SEGMENTATION USING A MULTI NETWORK SEGMENT CLASSIFICATION APPROACH},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2012) - Volume 2: VISAPP},
year={2012},
pages={521-530},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003833705210530},
isbn={978-989-8565-03-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2012) - Volume 2: VISAPP
TI - LEARNING OBJECT SEGMENTATION USING A MULTI NETWORK SEGMENT CLASSIFICATION APPROACH
SN - 978-989-8565-03-7
IS - 2184-4321
AU - Albertini, S.
AU - Gallo, I.
AU - Vanetti, M.
AU - Nodari, A.
PY - 2012
SP - 521
EP - 530
DO - 10.5220/0003833705210530
PB - SciTePress