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Enhancing CSS-based shape retrieval for objects with shallow concavities
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DOI：10.1016/S0262-8856(99)00019-0.png)
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Curvature scale space (CSS) image is a multi-scale organisation of the inflection points of a closed planar curve as it is smoothed. It consists of several arch shape contours, each related to a concavity or a convexity of the curve. The maxima of these contours have already been used as shape descriptors to find similar shapes in large image databases. In this article, we address the problem of shallow concavities. These may give rise to large contours in the CSS image. These contours may then match those corresponding to deep and wide concavities during the matching process. The phenomenon can be explained by recalling the fact that Gaussian smoothing leads to an approximation of geometric heat equation deformation. We have introduced a method to enrich the CSS image and create different contours for different types of concavities. We tested the proposed method on a database of 1100 images of marine creatures. A significant improvement was observed in the performance of the system on shapes with shallow segments. (C) 2000 Elsevier Science B.V. All rights reserved.
Keyword:
multi-scale analysis
shape similarity
curvature scale space
image database retrieval
performance characterisation
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