An efficient CHARM algorithm for indexation 2D/3D and selection of characteristic views

This paper presents a new classification and search method of 3D object features views. This method is an application of algorithms: • Charm for an object views classification purpose • Algorithm for extracting association rules in order to extract the characteristic view. We use the geometric descriptor of Zernike Moments to index 2D views of 3D object. The proposed method relies on a Bayesian probabilistic approach for search queries. The resulting outcome is presented by a collection of 120 3D models of the Princeton-based benchmark and then compared to those obtained from conventional methods.

[1]  Atilla Baskurt,et al.  SEMANTIC-3D: compression, indexation et tatouage de données 3D , 2004 .

[2]  André Hardy,et al.  Data mining et analyse des données: pour une nouvelle méthode de classification automatique des données symboliques intervalles, basée sur le processus de Poisson homogène - Thèse de doctorat. , 2009 .

[3]  Mohamed Daoudi,et al.  A Bayesian 3-D Search Engine Using Adaptive Views Clustering , 2007, IEEE Transactions on Multimedia.

[4]  Whoi-Yul Kim,et al.  A region-based shape descriptor using Zernike moments , 2000, Signal Process. Image Commun..

[5]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[6]  Mohamed Daoudi,et al.  A Bayesian framework for 3D models retrieval based on characteristic views , 2004, Proceedings. 2nd International Symposium on 3D Data Processing, Visualization and Transmission, 2004. 3DPVT 2004..

[7]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[8]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[9]  Mohammed J. Zaki,et al.  Efficient algorithms for mining closed itemsets and their lattice structure , 2005, IEEE Transactions on Knowledge and Data Engineering.

[10]  Bernard Chazelle,et al.  Matching 3D models with shape distributions , 2001, Proceedings International Conference on Shape Modeling and Applications.

[11]  O. Teytaud,et al.  Évaluation et validation de l'intérêt des règles d'association , 2003 .

[12]  Titus B. Zaharia,et al.  3D versus 2D/3D shape descriptors: a comparative study , 2004, IS&T/SPIE Electronic Imaging.

[13]  Alireza Khotanzad,et al.  Invariant Image Recognition by Zernike Moments , 1990, IEEE Trans. Pattern Anal. Mach. Intell..