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Automated image annotation using global features and robust nonparametric density estimation

Yavlinsky, Alexei; Schofield, Edward and Rüger, Stefan (2005). Automated image annotation using global features and robust nonparametric density estimation. In: Image and Video Retrieval, Lecture Notes in Computer Science, Springer, Berlin, pp. 507–517.

DOI (Digital Object Identifier) Link: http://doi.org/10.1007/11526346_54
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Abstract

This paper describes a simple framework for automatically annotating images using non-parametric models of distributions of image features. We show that under this framework quite simple image properties such as global colour and texture distributions provide a strong basis for reliably annotating images. We report results on subsets of two photographic libraries, the Corel Photo Archive and the Getty Image Archive. We also show how the popular Earth Mover’s Distance measure can be effectively incorporated within this framework.

Item Type: Conference Item
ISBN: 3-540-27858-3, 978-3-540-27858-0
Academic Unit/Department: Knowledge Media Institute
Item ID: 9623
Depositing User: Users 6898 not found.
Date Deposited: 03 Oct 2007
Last Modified: 22 Mar 2016 16:47
URI: http://oro.open.ac.uk/id/eprint/9623
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