Contour Salience Descriptors for Effective Image Retrieval and Analysis
R. da S. Torres, and A. X. Falcão, Contour Salience
Descriptors for Effective Image Retrieval and Analysis, Image
Vision Computing, 25(1):3-13, January, 2007.
Abstract
This work exploits the resemblance between content-based image
retrieval and image analysis with respect to the design of image
descriptors and their effectiveness. In this context, two shape
descriptors are proposed: contour saliences and segment
saliences. Contour saliences revisits its original definition, where
the location of concave points was a problem, and provides a robust
approach to incorporate concave saliences. Segment saliences
introduces salience values for contour segments, making it possible to
use an optimal matching algorithm as distance function. The proposed
descriptors are compared with convex contour saliences, curvature
scale space, and beam angle statistics using a fish database with
11,000 images organized in 1,100 distinct classes. The results
indicate segment saliences as the most effective descriptor for this
particular application and confirm the improvement of the contour
salience descriptor in comparison with convex contour saliences.
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Last updated on Nov 17, 2006.
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