Abstract
Different classes of image-distance functions are often used in computer vision. Robust pictorial indexes can be also constructed based on distance criteria for image retrieval purposes. This paper introduces two new classes of entropic distances that are based on the concept of convex transformations. Their formal properties are studied and tested on real images. Experiments on the comparison of images and the matching of objects are presented. A comparison of image distances, here, is proposed and carried out with measures of closeness.
Keywords
- Image Distance
- Normalize Distance Function
- Entropic Distance
- Good Discrimination Performance
- Convex Transformation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2002 Springer-Verlag Berlin Heidelberg
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Di Gesú, V., Roy, S. (2002). Pictorial Indexes and Soft Image Distances. In: Pal, N.R., Sugeno, M. (eds) Advances in Soft Computing — AFSS 2002. AFSS 2002. Lecture Notes in Computer Science(), vol 2275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45631-7_48
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DOI: https://doi.org/10.1007/3-540-45631-7_48
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Print ISBN: 978-3-540-43150-3
Online ISBN: 978-3-540-45631-5
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