Shape recognition based on Kernel-edit distance

In this paper a kernel method for shape recognition is proposed. The approach is based on the edit distance between pairs of shapes after transforming them into symbol strings. The transformation of shapes into symbol strings is invariant to similarity transforms and can handle partial occlusions. Representation of shape contours uses the shape contexts and applies dynamic programming for finding the correspondence between points over shape contours. Corresponding points are then transformed into symbolic representation and the normalized edit distance computes the dissimilarity between pairs of strings in the database. Obtained distances are then transformed into suitable kernels which are classified using support vector machines. Experimental results over a variety of shape databases show that the proposed approach is suitable for shape recognition.

[1]  Euripides G. M. Petrakis,et al.  Matching and Retrieval of Distorted and Occluded Shapes Using Dynamic Programming , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Zhuowen Tu,et al.  Improving Shape Retrieval by Learning Graph Transduction , 2008, ECCV.

[3]  Mehryar Mohri,et al.  Positive Definite Rational Kernels , 2003, COLT.

[4]  Boaz J. Super.  Learning Chance Probability Functions for Shape Retrieval or Classification , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[5]  Ryan M. Rifkin,et al.  In Defense of One-Vs-All Classification , 2004, J. Mach. Learn. Res..

[6]  Herbert Freeman,et al.  Computer Processing of Line-Drawing Images , 1974, CSUR.

[7]  Boaz J. Super,et al.  Classification of contour shapes using class segment sets , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[9]  Françoise Peyrin,et al.  A new method for analyzing local shape in three-dimensional images based on medial axis transformation , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Shi-Jinn Horng,et al.  Run-length chain coding and scalable computation of a shape's moments using reconfigurable optical buses , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Hong Yan,et al.  Skeletonization of ribbon-like shapes based on regularity and singularity analyses , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[13]  Mohammad Reza Daliri,et al.  Shape Categorization Using String Kernels , 2006, SSPR/SPR.

[14]  Philip N. Klein,et al.  Shock-Based Indexing into Large Shape Databases , 2002, ECCV.

[15]  Longin Jan Latecki,et al.  Locally constrained diffusion process on locally densified distance spaces with applications to shape retrieval , 2009, CVPR.

[16]  Ulrich Eckhardt,et al.  Shape descriptors for non-rigid shapes with a single closed contour , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[17]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[18]  Nicolai Petkov,et al.  Robustness of shape descriptors to incomplete contour representations , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Boaz J. Super,et al.  Improving object recognition accuracy and speed through nonuniform sampling , 2003, SPIE Optics East.

[20]  Stephen P. Boyd,et al.  Optimal kernel selection in Kernel Fisher discriminant analysis , 2006, ICML.

[21]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[23]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[24]  Pepe Siy,et al.  Robust shape similarity retrieval based on contour segmentation polygonal multiresolution and elastic matching , 2005, Pattern Recognit..

[25]  Miroslaw Bober,et al.  Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization , 2011, Computational Imaging and Vision.

[26]  Cordelia Schmid,et al.  Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[27]  Peter N. Yianilos,et al.  Learning String-Edit Distance , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[29]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[30]  Haibin Ling,et al.  Shape Classification Using the Inner-Distance , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Philip N. Klein,et al.  Recognition of shapes by editing their shock graphs , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Mohammad Reza Daliri,et al.  Robust symbolic representation for shape recognition and retrieval , 2008, Pattern Recognit..

[33]  Boaz J. Super.  Retrieval from Shape Databases Using Chance Probability Functions and Fixed Correspondence , 2006, Int. J. Pattern Recognit. Artif. Intell..

[34]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[35]  Bernt Schiele,et al.  Analyzing appearance and contour based methods for object categorization , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[36]  R. Basri,et al.  Shape representation and classification using the Poisson equation , 2004, CVPR 2004.

[37]  Mohammad Reza Daliri,et al.  Shape Recognition and Retrieval Using String of Symbols , 2006, 2006 5th International Conference on Machine Learning and Applications (ICMLA'06).

[38]  Horst Bunke,et al.  Edit distance-based kernel functions for structural pattern classification , 2006, Pattern Recognit..