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.
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Yavlinsky, A., Schofield, E., Rüger, S. (2005). Automated Image Annotation Using Global Features and Robust Nonparametric Density Estimation. In: Leow, WK., Lew, M.S., Chua, TS., Ma, WY., Chaisorn, L., Bakker, E.M. (eds) Image and Video Retrieval. CIVR 2005. Lecture Notes in Computer Science, vol 3568. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11526346_54
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DOI: https://doi.org/10.1007/11526346_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27858-0
Online ISBN: 978-3-540-31678-7
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