Nurbs: a New Shape Descriptor for Shape-based Image Retrieval
نویسندگان
چکیده
The representation, matching and analysis of objects of interest are of prime importance in shape-based retrieval systems. In the large image repositories, there arises a problem to find a set of images that are relevant to the user’s needs. This necessitates an effective retrieval approach that resembles the human capability to retrieving images that are relevant to a query from a database. Perceiving a shape is to capture prominent elements of an object. For the purpose of retrieval by shape similarity, representation is preferred such that the salient perceptual aspects of a shape are captured and are able to imitate the human perception in perceiving shapes. The representation method is important, because the effectiveness of the representation will determine the accuracy of the retrieval results. Thus, there is a need to have an effective and accurate representation method. The representation features are then used to compute the similarity score between two images. In this paper, we present a new shape descriptor to represent all possible shapes using Non-Uniform Rational B-Spline (NURBS). We also present NURBS-Warping method, which is similar to elastic matching, to obtain similarity score in the retrieval process. We run two sets of experiments to show the efficiency of NURBS shape representation and NURBS-Warping method over B-Spline representation.
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