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Directed Search in a 3D Objects Database Using SVM
Elad, Michael; Tal, Ayellet; Ar, Sigal
Keyword(s): 3D objects; search in databases; moments; support vector machine; quadratic programming
Abstract: This paper introduces a content-based search algorithm for a database of 3D objects. The search is performed by giving an example object, and looking for similar ones in the database. The search system result is given as several nearest neighbor objects. The weighted Euclidean distance between a sequence of normalized moments is used to measure the similarity between objects. The moments are estimated based on uniformly distributed random generation of 3D points on the objects' surface. An important feature of the search system is the proposed iterative refinement algorithm. Marking successful and failure decisions on previous results, this user's feedback is used to adapt the weights of the distance measure. The adaptation causes the successful objects to become nearer, and the failure decisions to become distant. Training the distance measure is done using the well- known SVM algorithm, which introduces robustness to the weight parameters. The above process may be repeated several times, accumulating 'Good' and 'Bad' examples and updating the distance measure to discriminate between the two with a maximal margin. This way, for each search and for each different user applying it, a different measure of similarity that reflects the user's desires and the searched object properties, is obtained. Simulations done on a database of more than 500 objects show promising results. It is shown that with only 2-3 such refinement iterations, the search results are successfully directed towards better suited 3D objects.
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