Abstract
Hierarchical image structures are abundant in computer vision, and have been used to encode part structure, scale spaces, and a variety of multiresolution features. In this paper, we describe a unified framework for both indexing and matching such structures. First, we describe an indexing mechanism that maps the topological structure of a directed acyclic graph (DAG) into a low-dimensional vector space. Based on a novel eigenvalue characterization of a DAG, this topological signature allows us to efficiently retrieve a small set of candidates from a database of models. To accommodate occlusion and local deformation, local evidence is accumulated in each of the DAG’s topological subspaces. Given a small set of candidate models, we will next describe a matching algorithm that exploits this same topological signature to compute, in the presence of noise and occlusion, the largest isomorphic subgraph between the image structure and the candidate model structure which, in turn, yields a measure of similarity which can be used to rank the candidates. We demonstrate the approach with a series of indexing and matching experiments in the domains of 2-D and (view-based) 3-D generic object recognition.
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References
F. Alizadeh. Interior point methods in semidefinite programming with applications to combinatorial optimization. SIAM J. Optim., 5(1):13–51, 1995.
K.L. Boyer and A.C. Kak. Structural stereopsis for 3-D vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(2):144–166, March 1988.
E. Chang, Stephane Mallat, and Chee Yap. Wavelet foveation. Journal of Applied and Computational Harmonic Analysis, 9(3):312–335, October 2000.
W. J. Christmas, J. Kittler, and M. Petrou. Structural matching in computer vision using probabilistic relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17:749–764, August 1995.
A.D. Cross and E.R. Hancock. Graph matching with a dual-step em algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11):1236–1253, November 1998. Fig.5 Results of matching the first two, and first and third images, respectively, in Figure 4. The first two images are taken from similar viewpoints (approx 15° apart), and hence many correspondences were found. However, for the first and third images, taken from different viewpoints (approx 75#x00B0 apart), fewer corresponding features were found.
S. Dickinson, A. Pentland, and A. Rosenfeld. From volumes to views: An approach to 3-D object recognition. CVGIP: Image Understanding, 55(2):130–154, 1992.
M. A. Eshera and K. S. Fu. A graph distance measure for image analysis. IEEE Trans. SMC, 14:398–408, May 1984.
P. Flynn and A. Jain. 3D object recognition using invariant feature indexing of interpretation tables. CVGIP:Image Understanding, 55(2):119–129, March 1992.
H. Gabow, M. Goemans, and D. Williamson. An eficient approximate algorithm for survivable network design problems. Proc. of the Third MPS Conference on Integer Programming and Combinatorial Optimization, pages 57–74, 1993.
Steven Gold and Anand Rangarajan. A graduated assignment algorithm for graph matching. IEEE PAMI, 18(4):377–388, 1996.
W. Kim and A. C. Kak. 3d object recognition using bipartite matching embedded in discrete relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(3):224–251, 1991.
B. B. Kimia, A. Tannenbaum, and S. W. Zucker. Shape, shocks, and deformations I: The components of two-dimensional shape and the reaction-diffusion space. International Journal of Computer Vision, 15:189–224, 1995.
Y. Lamdan, J. Schwartz, and H. Wolfson. Affine invariant model-based object recognition. IEEE Transactions on Robotics and Automation, 6(5):578–589, October 1990.
Tony Lindeberg. Detecting Salient Blob—Like Image Structures and Their Scales With a Scale-Space Primal SketchA Method for Focus—of-Attention. International Journal of Computer Vision, 11(3):283–318, December 1993.
L. Lovász and J. Pelicán. On the eigenvalues of a tree. Periodica Math. Hung., 3:1082–1096, 1970.
B. T. Messmer and H. Bunke. A new algorithm for error-tolerant subgraph isomorphism detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20:493–504, May 1998.
H. Murase and S. Nayar. Visual learning and recognition of 3-D objects from appearance. International Journal of Computer Vision, 14:5–24, 1995.
A. Neumaier. Second largest eigenvalue of a tree. Linear Algebra and its Applications, 46:9–25, 1982.
M. L. Overton and R. S. Womersley. Optimality conditions and duality theory for minimizing sums of the largest eigenvalues of symmetric matrices. Math. Programming, 62(2):321–357, 1993.
M. Pelillo, K. Siddiqi, and S. Zucker. Matching hierarchical structures using association graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(11):1105–1120, November 1999.
F. Preparata and M. Shamos. Computational Geometry. Springer-Verlag, New York, NY, 1985.
Recognition and shape synthesis of 3D objects based on attributed hypergraphs. A. Wong and S. Lu and M. Rioux. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11:279–290, 1989.
S. W. Reyner. An analysis of a good algorithm for the subtree problem. SIAM J. Comput., 6:730–732, 1977.
A. Sanfeliu and K. S. Fu. A distance measure between attributed relational graphs for pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics, 13:353–362, May 1983.
S. Sarkar. Learning to form large groups of salient image features. In IEEE CVPR, Santa Barbara, CA, June 1998.
S. Sclaro and A. Pentland. Modal matching for correspondence and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(6):545–561, June 1995.
K. Sengupta and K. Boyer. Using spectral features for modelbase partitioning. In Proceedings, International Conference on Pattern Recognition, Vienna, Austria, August 1996.
L. Shapiro and M. Brady. Feature-based correspondence: an eigenvector approach. Image and Vision Computing, 10(5):283–288, June 1992.
L. G. Shapiro and R. M. Haralick. Structural descriptions and inexact matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3:504–519, 1981.
L. G. Shapiro and R. M. Haralick. A metric for comparing relational descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7:90–94, January 1985.
J. Shi and J. Malik. Normalized cuts and image segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, June 1997.
A. Shokoufandeh and S. Dickinson. Applications of bipartite matching to problems in object recognition. In Proceedings, ICCV Workshop on Graph Algorithms and Computer Vision (web proceedings:http://www.cs.cornell.edu/iccv-graph-workshop/papers.htm), September 1999.
A. Shokoufandeh, S. Dickinson, K. Siddiqi, and S. Zucker. Indexing using a spectral encoding of topological structure. In IEEE Conference on Computer Vision and Pattern Recognition, pages 491–497, Fort Collins, CO, June 1999.
A. Shokoufandeh, I. Marsic, and S. Dickinson. View-based object recognition using saliency maps. Image and Vision Computing, 17:445–460, 1999.
K. Siddiqi, A. Shokoufandeh, S. Dickinson, and S. Zucker. Shock graphs and shape matching. International Journal of Computer Vision, 30:1–24, 1999.
H. Sossa and R. Horaud. Model indexing: The graph-hashing approach. In Proceedings, IEEE CVPR, pages 811–814, 1992.
G.W. Stewart and J.-G. Sun. Matrix Perturbation Theory. Academic Press, San Diego, 1990.
M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71–86, 1991.
J. Wilkinson. The Algebraic Eigenvalue Problem. Clarendon Press, Oxford, England, 1965.
A. Witkin. Scale space filtering. In Alex Pentland, editor, From Pixels to Predicates. Ablex, Norwood, NJ, 1986.
A. K. C. Wong and M. You. Entropy and distance of random graphs with application to structural pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7:599–609, September 1985.
S. Zhu and A. L. Yuille. Forms: a flexible object recognition and modelling system. International Journal of Computer Vision, 20(3):187–212, 1996.
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Shokoufandeh, A., Dickinson, S. (2001). A Unified Framework for Indexing and Matching Hierarchical Shape Structures. In: Arcelli, C., Cordella, L.P., di Baja, G.S. (eds) Visual Form 2001. IWVF 2001. Lecture Notes in Computer Science, vol 2059. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45129-3_6
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DOI: https://doi.org/10.1007/3-540-45129-3_6
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