View-based 3D model retrieval with probabilistic graph model

In this paper, we present a view-based 3D model retrieval algorithm using probabilistic graph model. In this work, five circle camera arrays are employed, and five groups of views are captured from each 3D model. Each captured view set is modeled as a first order Markov Chain. The task of 3D model retrieval is defined as a probabilistic analysis procedure, and the comparison between the query and other 3D models is changed to compute the conditional probabilities of 3D models in the database given the query model. The purpose to search 3D model is to find the maximal a posterior probability of the models in the database given the query model. Then, we present a solution to estimate the conditional probabilities. The proposed 3D model retrieval algorithm has been evaluated on the NTU 3D model database. Experimental results and comparison with other methods show the effectiveness of the proposed approach.

[1]  Ioannis A. Kakadiaris,et al.  Intraclass Retrieval of Nonrigid 3D Objects: Application to Face Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Ioannis Pratikakis,et al.  Efficient 3D shape matching and retrieval using a concrete radialized spherical projection representation , 2007, Pattern Recognit..

[3]  Marc Rioux,et al.  Description of shape information for 2-D and 3-D objects , 2000, Signal Process. Image Commun..

[4]  Yue Gao,et al.  3D model comparison using spatial structure circular descriptor , 2010, Pattern Recognit..

[5]  Shyi-Chyi Cheng,et al.  3D model retrieval using principal plane analysis and dynamic programming , 2007, Pattern Recognit..

[6]  Marc Rioux,et al.  Nefertiti: A tool for 3-D shape databases management , 1999 .

[7]  Remco C. Veltkamp,et al.  Polyhedral Model Retrieval Using Weighted Point Sets , 2003, Int. J. Image Graph..

[8]  Xuelong Li,et al.  Bayesian Tensor Approach for 3-D Face Modeling , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  In-So Kweon,et al.  Scalable representation for 3D object recognition using feature sharing and view clustering , 2008, Pattern Recognit..

[10]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Bernard Chazelle,et al.  Shape distributions , 2002, TOGS.

[12]  Alberto Del Bimbo,et al.  Content-based retrieval of 3D models , 2006, TOMCCAP.

[13]  William C. Regli,et al.  Using shape distributions to compare solid models , 2002, SMA '02.

[14]  Chang-Hsing Lee,et al.  A new 3D model retrieval approach based on the elevation descriptor , 2007, Pattern Recognit..

[15]  Xuelong Li,et al.  Probabilistic Tensor Analysis with Akaike and Bayesian Information Criteria , 2007, ICONIP.

[16]  Alberto Del Bimbo,et al.  Content-Based Retrieval of 3-D Objects Using Spin Image Signatures , 2007, IEEE Transactions on Multimedia.

[17]  J. Munkres.  ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS* , 1957 .

[18]  Cordelia Schmid,et al.  3D Object Modeling and Recognition Using Local Affine-Invariant Image Descriptors and Multi-View Spatial Constraints , 2006, International Journal of Computer Vision.

[19]  Dejan V. VraniC.  An improvement of rotation invariant 3D-shape based on functions on concentric spheres , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[20]  H. Kuhn.  The Hungarian method for the assignment problem , 1955 .

[21]  BENJAMIN BUSTOS,et al.  Feature-based similarity search in 3D object databases , 2005, CSUR.

[22]  Renato Pajarola,et al.  Confetti: object-space point blending and splatting , 2004, IEEE Transactions on Visualization and Computer Graphics.

[23]  Qionghai Dai,et al.  Weighted Subspace Distance and Its Applications to Object Recognition and Retrieval With Image Sets , 2009, IEEE Signal Processing Letters.

[24]  Ming Ouhyoung,et al.  On Visual Similarity Based 3D Model Retrieval , 2003, Comput. Graph. Forum.