3D model classification via Principal Thickness Images

With the innovation in 3D modeling software, more and more 3D models are becoming available in recent decades. To facilitate efficient retrieval and search of large 3D model databases, an effective shape classification algorithm is badly in need. In this paper, we propose a new feature descriptor named Principal Thickness Images (PTI) that encodes the boundary surface and the voxelized constituents of a 3D shape into three gray-scale images. With the support of PTI, we extend the kernel sparse representation-based classification from 2D case to non-rigid 3D models. Our classification algorithm inherits the robustness of kernel sparse representation and is able to achieve a high success rate and strong reliability on non-rigid models from the SHREC'11 non-rigid 3D models dataset. Numerous tests demonstrate superior performance of the proposed method over previous 3D shape classification approaches.

[1]  Xindong Wu,et al.  3-D Object Retrieval With Hausdorff Distance Learning , 2014, IEEE Transactions on Industrial Electronics.

[2]  Amaury Lendasse,et al.  3D object recognition based on a geometrical topology model and extreme learning machine , 2013, Neural Computing and Applications.

[3]  Michael C. Hout,et al.  Multidimensional Scaling , 2003, Encyclopedic Dictionary of Archaeology.

[4]  Paul Suetens,et al.  meshSIFT: Local surface features for 3D face recognition under expression variations and partial data , 2013, Comput. Vis. Image Underst..

[5]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[6]  Ting Wang,et al.  Kernel Sparse Representation-Based Classifier , 2012, IEEE Transactions on Signal Processing.

[7]  Nicu Sebe,et al.  Emotion recognition using a Cauchy Naive Bayes classifier , 2002, Object recognition supported by user interaction for service robots.

[8]  Hao Zhang,et al.  A spectral approach to shape-based retrieval of articulated 3D models , 2007, Comput. Aided Des..

[9]  Indriyati Atmosukarto,et al.  3D object retrieval using salient views , 2010, MIR '10.

[10]  Zhang Xiong,et al.  3D Object Classification Using Deep Belief Networks , 2014, MMM.

[11]  David A. Forsyth,et al.  Generalizing motion edits with Gaussian processes , 2009, ACM Trans. Graph..

[12]  Szymon Rusinkiewicz,et al.  Rotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors , 2003, Symposium on Geometry Processing.

[13]  Luc Van Gool,et al.  Orientation invariant 3D object classification using hough transform based methods , 2010, 3DOR '10.

[14]  Paul Suetens,et al.  SHREC '11 Track: Shape Retrieval on Non-rigid 3D Watertight Meshes , 2011, 3DOR@Eurographics.

[15]  Shi-Qing Xin,et al.  Improving Chen and Han's algorithm on the discrete geodesic problem , 2009, TOGS.

[16]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[17]  Yue Gao,et al.  3-D Object Retrieval and Recognition With Hypergraph Analysis , 2012, IEEE Transactions on Image Processing.

[18]  Sameer A. Nene,et al.  A simple algorithm for nearest neighbor search in high dimensions , 1997 .

[19]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[20]  Michael Garland,et al.  Surface simplification using quadric error metrics , 1997, SIGGRAPH.

[21]  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).

[22]  Atilla Baskurt,et al.  Generalizations of angular radial transform for 2D and 3D shape retrieval , 2005, Pattern Recognit. Lett..

[23]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[24]  Yue Gao,et al.  Learning-Based Bipartite Graph Matching for View-Based 3D Model Retrieval , 2014, IEEE Transactions on Image Processing.

[25]  Ling Shao,et al.  Learning View-Model Joint Relevance for 3D Object Retrieval , 2015, IEEE Transactions on Image Processing.

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

[27]  D. Donoho.  For most large underdetermined systems of equations, the minimal 𝓁1‐norm near‐solution approximates the sparsest near‐solution , 2006 .

[28]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

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

[30]  Daniel Cohen-Or,et al.  Consistent mesh partitioning and skeletonisation using the shape diameter function , 2008, The Visual Computer.

[31]  Ralph R. Martin,et al.  Euclidean-distance-based canonical forms for non-rigid 3D shape retrieval , 2015, Pattern Recognit..

[32]  Daniel Cohen-Or,et al.  Electors Voting for Fast Automatic Shape Correspondence , 2010, Comput. Graph. Forum.

[33]  Feng Zhang,et al.  Spectral Classification of 3D Articulated Shapes , 2014, MMM.

[34]  Szymon Rusinkiewicz,et al.  Symmetry descriptors and 3D shape matching , 2004, SGP '04.

[35]  Yiguang Liu,et al.  A novel and quick SVM-based multi-class classifier , 2006, Pattern Recognit..

[36]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[37]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Ke Lu,et al.  3D model retrieval and classification by semi-supervised learning with content-based similarity , 2014, Inf. Sci..

[39]  Edoardo Amaldi,et al.  On the Approximability of Minimizing Nonzero Variables or Unsatisfied Relations in Linear Systems , 1998, Theor. Comput. Sci..

[40]  Jun-Bao Li,et al.  3D model classification based on nonparametric discriminant analysis with kernels , 2011, Neural Computing and Applications.

[41]  David J. Fleet,et al.  Building proteins in a day: Efficient 3D molecular reconstruction , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[42]  Iasonas Kokkinos,et al.  Scale-invariant heat kernel signatures for non-rigid shape recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[43]  Subhransu Maji,et al.  Multi-view Convolutional Neural Networks for 3D Shape Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).