A similarity-based approach for shape classification using Aslan skeletons

Shape skeletons are commonly used in generic shape recognition as they capture part hierarchy, providing a structural representation of shapes. However, their potential for shape classification has not been investigated much. In this study, we present a similarity-based approach for classifying 2D shapes based on their Aslan skeletons (Aslan and Tari, 2005; Aslan et al., 2008). The coarse structure of this skeleton representation allows us to represent each shape category in the form of a reduced set of prototypical trees, offering an alternative solution to the problem of selecting the best representative examples. The ensemble of these category prototypes is then used to form a similarity-based representation space in which the similarities between a given shape and the prototypes are computed using a tree edit distance algorithm, and support vector machine (SVM) classifiers are used to predict the category membership of the shape based on computed similarities.

[1]  Longin Jan Latecki,et al.  Skeleton-Based Shape Classification Using Path Similarity , 2008, Int. J. Pattern Recognit. Artif. Intell..

[2]  Luiz A. Costa,et al.  Determining the similarity of deformable shapes , 1995, Vision Research.

[3]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[4]  Daphna Weinshall,et al.  Flexible Syntactic Matching of Curves and Its Application to Automatic Hierarchical Classification of Silhouettes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Robert Kohn,et al.  Representation and self-similarity of shapes , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[6]  Kaizhong Zhang,et al.  Tree pattern matching , 1997, Pattern Matching Algorithms.

[7]  Sibel Tari,et al.  An axis-based representation for recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[8]  R. Duin,et al.  The dissimilarity representation for pattern recognition , a tutorial , 2009 .

[9]  R. Nosofsky.  Attention, similarity, and the identification-categorization relationship. , 1986, Journal of experimental psychology. General.

[10]  Horst Bunke,et al.  Transforming Strings to Vector Spaces Using Prototype Selection , 2006, SSPR/SPR.

[11]  Gabriella Sanniti di Baja,et al.  Hierarchical Decomposition of Multiscale Skeletons , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Robert P. W. Duin,et al.  Prototype selection for dissimilarity-based classifiers , 2006, Pattern Recognit..

[13]  Stelios Krinidis,et al.  A Skeleton Family Generator via Physics-Based Deformable Models , 2009, IEEE Transactions on Image Processing.

[14]  Eleanor Rosch,et al.  Principles of Categorization , 1978 .

[15]  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).

[16]  Edward E. Smith,et al.  Categories and concepts , 1984 .

[17]  Robert P. W. Duin,et al.  Featureless pattern classification , 1998, Kybernetika.

[18]  R. Luce,et al.  The Choice Axiom after Twenty Years , 1977 .

[19]  Kaleem Siddiqi,et al.  Hamilton-Jacobi Skeletons , 2002, International Journal of Computer Vision.

[20]  R. Sternberg,et al.  The nature of cognition , 2000 .

[21]  Mohammad Reza Daliri,et al.  Classification of silhouettes using contour fragments , 2009, Comput. Vis. Image Underst..

[22]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[23]  Horst Bunke,et al.  Towards Bridging the Gap between Statistical and Structural Pattern Recognition: Two New Concepts in Graph Matching , 2001, ICAPR.

[24]  Alan L. Yuille,et al.  FORMS: A flexible object recognition and modelling system , 1996, International Journal of Computer Vision.

[25]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[26]  J. Tenenbaum,et al.  Generalization, similarity, and Bayesian inference. , 2001, The Behavioral and brain sciences.

[27]  Robert P. W. Duin,et al.  The Dissimilarity Representation for Pattern Recognition - Foundations and Applications , 2005, Series in Machine Perception and Artificial Intelligence.

[29]  David G. Stork,et al.  Pattern Classification , 1973 .

[30]  Wenyu Liu,et al.  Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  R. Weale.  Vision. A Computational Investigation Into the Human Representation and Processing of Visual Information. David Marr , 1983 .

[32]  Jayant Shah,et al.  Extraction of Shape Skeletons from Grayscale Images , 1997, Comput. Vis. Image Underst..

[33]  Longin Jan Latecki,et al.  Path Similarity Skeleton Graph Matching , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Alfred M. Bruckstein,et al.  Pruning Medial Axes , 1998, Comput. Vis. Image Underst..

[35]  N. Chater,et al.  Similarity as transformation , 2003, Cognition.

[36]  Ali Shokoufandeh,et al.  Shock Graphs and Shape Matching , 1998, International Journal of Computer Vision.

[37]  Aykut Erdem,et al.  Dissimilarity between two skeletal trees in a context , 2009, Pattern Recognit..

[38]  Zhuowen Tu,et al.  Active skeleton for non-rigid object detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[39]  A. Tversky.  Features of Similarity , 1977 .

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

[41]  Taku Komura,et al.  Topology matching for fully automatic similarity estimation of 3D shapes , 2001, SIGGRAPH.

[42]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[43]  M. Fatih Demirci,et al.  Indexing through laplacian spectra , 2008, Comput. Vis. Image Underst..

[44]  Enrique Vidal,et al.  Learning prototypes and distances (LPD). A prototype reduction technique based on nearest neighbor error minimization , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

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

[46]  R. Shepard,et al.  Toward a universal law of generalization for psychological science. , 1987, Science.

[47]  Longin Jan Latecki,et al.  Shape Similarity Measure Based on Correspondence of Visual Parts , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

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

[49]  Sven J. Dickinson,et al.  From skeletons to bone graphs: Medial abstraction for object recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

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

[51]  Z. Galil,et al.  Pattern matching algorithms , 1997 .

[52]  Robert L. Goldstone.  Similarity, interactive activation, and mapping , 1994 .

[53]  R. Duncan Luce,et al.  Individual Choice Behavior: A Theoretical Analysis , 1979 .

[54]  Ali Shokoufandeh,et al.  Indexing hierarchical structures using graph spectra , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[55]  Shimon Edelman,et al.  Representation and recognition in vision , 1999 .

[56]  Anuj Srivastava,et al.  Statistical shape analysis: clustering, learning, and testing , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[57]  Ricardo A. Baeza-Yates,et al.  Searching in metric spaces , 2001, CSUR.

[58]  Jitendra Malik,et al.  SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[59]  Douglas L. Medin,et al.  Context theory of classification learning. , 1978 .

[60]  Linda B. Smith,et al.  The importance of shape in early lexical learning , 1988 .

[61]  Zhuowen Tu,et al.  Integrating contour and skeleton for shape classification , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[62]  B. Kimia,et al.  3D object recognition using shape similiarity-based aspect graph , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[63]  Kaleem Siddiqi,et al.  Ligature instabilities in the perceptual organization of shape , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[64]  Matthew Turk,et al.  Shape classification through structured learning of matching measures , 2009, CVPR.

[65]  Aykut Erdem,et al.  Disconnected Skeleton: Shape at Its Absolute Scale , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[66]  Kaizhong Zhang,et al.  Approximate tree pattern matching , 1997 .

[67]  Ghassan Hamarneh,et al.  The Groupwise Medial Axis Transform for Fuzzy Skeletonization and Pruning , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[68]  E. Rosch,et al.  Cognition and Categorization , 1980 .

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

[70]  J. Kruschke,et al.  ALCOVE: an exemplar-based connectionist model of category learning. , 1992, Psychological review.

[71]  R. Luce,et al.  Individual Choice Behavior: A Theoretical Analysis. , 1960 .