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Authors: Iuri Frosio 1 and Ed R. Ratner 2

Affiliations: 1 NVIDIA, United States ; 2 Lyrical Labs, United States

ISBN: 978-989-758-089-5

Keyword(s): Superpixel, Adaptive Segmentation, Machine Learning, Segmentation Quality Metric.

Related Ontology Subjects/Areas/Topics: Color and Texture Analyses ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Segmentation and Grouping ; Visual Attention and Image Saliency

Abstract: We introduce here a model for the evaluation of the segmentation quality of a color image. The model parameters were learned from a set of examples. To this aim, we first segmented a set of images using a traditional graph-cut algorithm, for different values of the scale parameter. A human observer classified these images into three classes: under-, well- and over-segmented. This classification was employed to learn the parameters of the segmentation quality model. This was used to automatically optimize the scale parameter of the graph-cut segmentation algorithm, even at a local scale. Experimental results show an improved segmentation quality for the adaptive algorithm based on our segmentation quality model, which can be easily applied to a wide class of segmentation algorithms.

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Paper citation in several formats:
Frosio, I. and Ratner, E. (2015). Adaptive Segmentation based on a Learned Quality Metric.In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 283-292. DOI: 10.5220/0005257202830292

@conference{visapp15,
author={Iuri Frosio. and Ed R. Ratner.},
title={Adaptive Segmentation based on a Learned Quality Metric},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={283-292},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005257202830292},
isbn={978-989-758-089-5},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Adaptive Segmentation based on a Learned Quality Metric
SN - 978-989-758-089-5
AU - Frosio, I.
AU - Ratner, E.
PY - 2015
SP - 283
EP - 292
DO - 10.5220/0005257202830292

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