Image Labeling using Integration of Local and Global Features

Takuto Omiya, Kazuhiro Hotta

2013

Abstract

In this paper, we carry out image labeling based on probabilistic integration of local and global features. Many conventional methods put label to each pixel or region using the features extracted from local regions and local contextual relationships between neighboring regions. However, labeling results tend to depend on a local viewpoint. To overcome this problem, we propose the image labeling method using not only local features but also global features. We compute posterior probability of local and global features independently, and they are integrated by the product. To compute probability of global region (entire image), Bag-of-Words is used. On the other hand, local co-occurrence between color and texture features is used to compute local probability. In the experiments using MSRC21 dataset, labeling accuracy is much improved by using global viewpoint.

Paper Citation

in Harvard Style

Omiya T. and Hotta K. (2013). Image Labeling using Integration of Local and Global Features . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 613-618. DOI: 10.5220/0004334606130618

in Bibtex Style

@conference{icpram13,
author={Takuto Omiya and Kazuhiro Hotta},
title={Image Labeling using Integration of Local and Global Features},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={613-618},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004334606130618},
isbn={978-989-8565-41-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Image Labeling using Integration of Local and Global Features
SN - 978-989-8565-41-9
AU - Omiya T.
AU - Hotta K.
PY - 2013
SP - 613
EP - 618
DO - 10.5220/0004334606130618