Abstract
In this paper we present a novel approach to shape representation based on correlating a set of object Regions of Interest (RoI) with a set of shape templates. The resultant correlations are the shape features used to build a Template-based Shape Feature Vector (TSFV) that represents the shape of the object. For each class of objects, a set of Main Shape Features (MSFs) is determined so that only the most descriptive features are used when comparing shapes. The proposed technique is tested on two benchmark databases, Kimia-99 and Kimia-216 and is shown to produce competitive results.KeywordsFeature PointBasic ShapeRetrieval AccuracyBenchmark DatabaseSearch ObjectThese 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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