Shape Image Retrieval Using Elastic Matching Combined with Snake Model
Shape-based recovery from image or video databases has become an important information retrieval problem. It is particularly challenging, owning to the difficulty to derive a similarity measurement that closely conforms to the common perception of humans. The goal of the current work is to achieve idea retrieval accuracy with reasonable speed and support for partial and occluded shapes. So, in this paper we introduce the elastic matching that is inspired by Duncan and Ayache combined with snake as a new shape retrieval technique. The elastic matching is to minimize of a quadratic fitting criterion, which consists of a curvature dependent bending energy term and a smoothness term. To reduce the computational complexity, the equation corresponding is only to the minimization of one-dimensional fitting criterion. As a result, the method proposed has the advantage of retrieve resemble objects with reasonable speed and less training samples.
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