Searching in a spatial database for 3D objects that are similar to a given object is an important task that arises in a number of database applications for example in medicine and CAD fields. Most of the existing similarity searching methods are based on global features of 3D objects. Developing a feature set or a feature vector of 3D object using their partial features is a challenging. In this paper we propose a novel segment weight vector for matching 3D objects rapidly. We also describe a partial and geometrical similarity based solution to the problem of searching for similar 3D objects. As the first step we split each 3D object into parts according to its topology. Next we introduce a new method to extract the thickness feature of each part of every 3D object to generate its feature vector and a novel searching algorithm using the new feature vector. Finally we present a novel solution for improving the accuracy of the similarity queries. We also present a performance evaluation of our stratagem. The experiment result and discussion indicate that the proposed approach offers a significant performance improvement over the existing approach. Since the proposed method is based on partial features it is particularly suited to searching objects having distinct part structures and is invariant to part architecture.

Searching in a spatial database for 3D objects that are similar to a given object is an important task that arises in a number of database applications, for example, in medicine and CAD fields. Most of the existing similarity searching methods are based on global features of 3D objects. Developing a feature set or a feature vector of 3D object using their partial features is a challenging. In this paper, we propose a novel segment weight vector for matching 3D objects rapidly. We also describe a partial and geometrical similarity based solution to the problem of searching for similar 3D objects. As the first step, we split each 3D object into parts according to its topology. Next, we introduce a new method to extract the thickness feature of each part of every 3D object to generate its feature vector and a novel searching algorithm using the new feature vector. Finally, we present a novel solution for improving the accuracy of the similarity queries. We also present a performance evaluation of our stratagem. The experiment result and discussion indicate that the proposed approach offers a significant performance improvement over the existing approach. Since the proposed method is based on partial features, it is particularly suited to searching objects having distinct part structures and is invariant to part architecture.