Marcello Pelillo, Selected Publications

Books

1. M. Pelillo and E. R. Hancock (Eds.),
   Energy Minimization Methods in Computer Vision and Pattern Recognition -- EMMCVPR'97.
   Lecture Notes in Computer Science, Vol. 1223.
   Springer-Verlag, Berlin, 1997.

2. E. R. Hancock and M. Pelillo (Eds.),
   Energy Minimization Methods in Computer Vision and Pattern Recognition -- EMMCVPR'99.
   Lecture Notes in Computer Science, Vol. 1654.
   Springer-Verlag, Berlin, 1999.

Special Issues of Journals Edited

1. E. R. Hancock and M. Pelillo (Guest Editors),
   Pattern Recognition (Impact factor = 3.279)
   Special Issue on Energy Minimization Methods in Computer Vision and Pattern Recognition.
   Vol. 33, No. 4, April 2000.
   Editorial

2. S. Dickinson, M. Pelillo, and R. Zabih (Guest Editors),
   IEEE Transactions on Pattern Analysis and Machine Intelligence (Impact factor = 5.960)
   Special Issue on Graph Algorithms in Computer Vision.
   Vol. 23, No. 10, October 2001.
   Editorial

3. M. Figueiredo, E. R. Hancock, M. Pelillo, and J. Zerubia (Guest Editors),
   IEEE Transactions on Pattern Analysis and Machine Intelligence (Impact factor = 5.960)
   Special Issue on Energy Minimization Methods in Computer Vision and Pattern Recognition.
   Vol. 25, No. 11, November 2003 (Part I) Editorial
   Vol. 26, No. 2, February 2004 (Part II) Editorial

4. M. Bicego, V. Murino, M. Pelillo, and A. Torsello (Guest Editors),
   Pattern Recognition (Impact factor = 3.279)
   Special Issue on Similarity-Based Pattern Recognition.
   Vol. 39, No. 10, October 2006.
   Editorial

Chapters in Books

1. I. M. Bomze, M. Budinich, P. M. Pardalos, and M. Pelillo,
   The maximum clique problem,
   Handbook of Combinatorial Optimization (Supplement Volume A),
   D.-Z. Du and P. M. Pardalos (Eds.),
   Kluwer Academic Publishers, Boston, MA, pp. 1-74, 1999.
   

2. M. Pelillo,
   Heuristics for maximum clique and independent set,
   Encyclopedia of Optimization,
   C. A. Floudas and P. M. Pardalos (Eds.),
   Kluwer Academic Publishers, Boston, MA, Vol. 2, pp.411-423, 2001.
   

3. M. Pelillo,
   Replicator dynamics in combinatorial optimization,
   Encyclopedia of Optimization,
   C. A. Floudas and P. M. Pardalos (Eds.),
   Kluwer Academic Publishers, Boston, MA, Vol. 5, pp. 23-35, 2001.
   

4. A. Massaro and M. Pelillo,
   A pivoting-based heuristic for the maximum clique problem,
   Advances in Convex Analysis and Global Optimization (Chap. 23),
   N. Hadjisavvas and P. M. Pardalos (Eds.),
   Kluwer Academic Publishers, Boston, MA, pp. 383-394, 2001.

5. M. Pelillo,
   Computational complexity and the elusiveness of global optima,
   Limitations and Future Trends in Neural Computation (Chap. 4),
   S. Ablameyko, M. Gori, L. Goras, and V. Piuri (Eds.),
   IOS Press, Amsetrdam, The Netherlands, pp. 71-93, 2003.

Journal Papers

1. M. Pelillo and M. Refice,
   Learning compatibility coefficients for relaxation labeling processes.
   IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(9):933-945, 1994.
   (Impact factor = 5.960)

2. M. Pelillo, F. Abbattista, and A. Maffione,
   An evolutionary approach to training relaxation labeling processes,
   Pattern Recognition Letters, 16(10):1069-1078, 1995.
   (Impact factor = 1.559)

3. G. Castellano, A. M. Fanelli, and M. Pelillo,
   Iterative pruning in second-order recurrent neural networks,
   Neural Processing Letters, 2(6):5-8, 1995.
   (Impact factor = 0.942)

4. M. Pelillo,
   Relaxation labeling networks for the maximum clique problem,
   Journal of Artificial Neural Networks, 2(4):313-328, 1995.
   Special issue on "Neural Networks for Optimization."
   (Impact factor = N/A)

5. M. Pelillo and A. Jagota,
   Feasible and infeasible maxima in a quadratic program for maximum clique,
   Journal of Artificial Neural Networks, 2(4):411-420, 1995.
   Special issue on "Neural Networks for Optimization."
   (Impact factor = N/A)

6. M. Pelillo,
   A relaxation algorithm for estimating the domain of validity of feedforward neural networks,
   Neural Processing Letters, 3:113-121, 1996.
   (Impact factor = 0.942)

7. M. Pelillo and A. M. Fanelli,
   Autoassociative learning in relaxation labeling networks,
   Pattern Recognition Letters, 18(1):3-12, 1997.
   (Impact factor = 1.559)

8. G. Castellano, A. M. Fanelli, and M. Pelillo,
   An iterative pruning algorithm for feedforward neural networks,
   IEEE Transactions on Neural Networks, 8(3):519-531, 1997.
   (Impact factor = 3.726)

9. M. Pelillo,
   The dynamics of nonlinear relaxation labeling processes.
   Journal of Mathematical Imaging and Vision, 7(4):309-323, 1997.
   (Impact factor = 1.331)

10. E. R. Hancock and M. Pelillo,
    A Bayesian interpretation for the exponential correlation associative memory.
    Pattern Recognition Letters, 19(2):149-159, 1998.
    (Impact factor = 1.559)

11. M. Pelillo,
    Replicator equations, maximal cliques, and graph isomorphism.
    Neural Computation, 11(8):1933-1955, 1999.
    (Impact factor = 2.378)

12. M. Pelillo, K. Siddiqi, and S. W. Zucker,
    Matching hierarchical structures using association graphs,
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(11):1105-1120, 1999.
    (Impact factor = 5.960)

13. A. Torsello and M. Pelillo,
    Continuous-time relaxation labeling processes.
    Pattern Recognition, 33(11):1897-1908, 2000.
    (Impact factor = 3.279)

14. I. M. Bomze, M. Pelillo, and V. Stix,
    Approximating the maximum weight clique using replicator dynamics,
    IEEE Transactions on Neural Networks, 11(6):1228-1241, 2000.
    (Impact factor = 3.726)

15. A. Massaro, M. Pelillo, and I. M. Bomze,
    A complementary pivoting approach to the maximum weight clique problem.
    SIAM Journal on Optimization, 12(4):928-948, 2002.
    (Impact factor = 1.525)

16. I. M. Bomze, M. Budinich, M. Pelillo, and C. Rossi,
    Annealed replication: A new heuristic for the maximum clique problem,
    Discrete Applied Mathematics, 121(1-3):27-49, 2002.
    (Impact factor = 0.783)

17. M. Pelillo,
    Matching free trees, maximal cliques, and monotone game dynamics,
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(11):1535-1541, 2002.
    (Impact factor = 5.960)

18. A. Massaro and M. Pelillo,
    Matching graphs by pivoting,
    Pattern Recognition Letters, 24(8):1099-1106, 2003.
    (Impact factor = 1.559)

19. D. Hidovic and M. Pelillo,
    Metrics for attributed graphs based on the maximal similarity common subgraph,
    International Journal of Pattern Recognition and Artificial Intelligence, 18(3):299-313, 2004.
    Special issue on "Graph Matching in Computer Vision and Pattern Recognition."
    (Impact factor = 0.660)

20. R. Glantz, M. Pelillo, and W. G. Kropatsch,
    Matching segmentation hierarchies,
    International Journal of Pattern Recognition and Artificial Intelligence, 18(3):397-424, 2004.
    Special issue on "Graph Matching in Computer Vision and Pattern Recognition."
    (Impact factor = 0.660)

21. M. Locatelli, I. M. Bomze, and M. Pelillo,
    The combinatorics of pivoting for the maximum weight clique.
    Operations Research Letters, 32:523-529, 2004.
    (Impact factor = 0.830)

22. A. Torsello, D. Hidovic-Rowe, and M. Pelillo,
    Polynomial-time metrics for attributed trees.
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(7):1087-1099, 2005.
    Special issue on "Syntactic and Structural Pattern Recognition."
    (Impact factor = 5.960)

23. M. Pelillo and A. Torsello
    Payoff-monotonic game dynamics and the maximum clique problem.
    Neural Computation, 18(5):1215-1258, 2006.
    (Impact factor = 2.378)

24. R. Glantz and M. Pelillo,
    Graph polynomials from principal pivoting.
    Discrete Mathematics, 306:3252-3266, 2006.
    (Impact factor = 0.502)

25. M. Pavan and M. Pelillo,
    Dominant sets and pairwise clustering.
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(1):167-172, 2007.
    (Impact factor = 5.960)

26. S. Rota Bulo' and M. Pelillo,
    A generalization of the Motzkin-Straus theorem to hypergraphs.
    Optimization Letters, 3(2):287-295 (2009).
    (Impact factor = 0.528)

27. S. Rota Bulo', A. Torsello, and M. Pelillo,
    A game-theoretic approach for partial clique enumeration.
    Image and Vision Computing, 27(7):911-922 (2009).
    (Impact factor = 1.496)

Selected Conference Papers

1. M. Pelillo and S. Scarci,
   Handling dictation ambiguities in the production of text from large-vocabulary speech recognition,
   Proc. VERBA90-Int. Conf. on Speech Technologies,
   Rome, Italy, 1990, pp. 380-384.

2. M. Pelillo and M. Refice,
   Syntactic category disambiguation through relaxation processes,
   Proc. EuroSpeech'91-2nd Europ. Conf. on Speech Commun. and Technologies,
   Genova, Italy, 1991, pp. 757-760.

3. M. Pelillo and M. Refice,
   An optimization algorithm for determining the compatibility coefficients of relaxation labeling processes,
   Proc. ICPR'92-11th Int. Conf. on Pattern Recognition,
   The Hague, The Netherlands, 1992, pp. 145-148.
   IEEE Computer Society Press.

4. M. Pelillo and M. Refice,
   Learning compatibility coefficients for word-class disambiguation relaxation processes,
   Proc. ICSLP'92-Int. Conf. on Spoken Language Processing,
   Banff, Canada, 1992, pp. 389-392.

5. M. Pelillo, F. Moro, and M. Refice,
   Probabilistic prediction of parts-of-speech from word-spelling using decision trees,
   Proc. ICSLP'92-Int. Conf. on Spoken Language Processing,
   Banff, Canada, 1992, pp. 1343-1346.

6. M. Pelillo, F. Abbattista, and A. Maffione,
   Evolutionary learning for relaxation labeling processes,
   Advances in Artificial Intelligence,
   P. Torasso (Ed.),
   (Lecture Notes in Artificial Intelligence, Vol. 728).
   Springer-Verlag, Berlin, 1993, pp. 230-241.

7. M. Pelillo,
   Relaxation labeling processes for the traveling salesman problem,
   Proc. IJCNN'93-1993 Int. J. Conf. on Neural Networks,
   Nagoya, Japan, 1993, pp. 2429-2432.

8. G. Castellano, A. M. Fanelli, and M. Pelillo,
   An empirical comparison of node pruning methods for layered feed-forward neural networks,
   Proc. IJCNN'93-1993 Int. J. Conf. on Neural Networks,
   Nagoya, Japan, 1993, pp. 321-326.

9. M. Pelillo and A. M. Fanelli,
   A method of pruning layered feed-forward neural networks,
   New Trends in Neural Computation,
   J. Mira, J. Cabestany, and A. Prieto (Eds.),
   (Lecture Notes in Computer Science, Vol. 686).
   Springer-Verlag, Berlin, 1993, pp. 278-283.

10. M. Pelillo, F. Abbattista, and N. Abbattista,
    Globally optimal learning for relaxation labeling by simulated annealing,
    Progress in Image Analysis and Processing III,
    S. Impedovo (Ed.),
    World Scientific, Singapore, 1994, pp. 241-247.

11. F. Abbattista, A. M. Fanelli, and M. Pelillo,
    An evolutionary approach to vector quantizer design,
    Progress in Image Analysis and Processing III,
    S. Impedovo (Ed.),
    World Scientific, Singapore, 1994, pp. 254-257.

12. M. Pelillo,
    Nonlinear relaxation labeling as growth transformation,
    Proc. ICPR'94-12th Int. Conf. on Pattern Recognition,
    Jerusalem, Israel, 1994, pp. 201-206.
    IEEE Computer Society Press.

13. M. Pelillo,
    On the dynamics of relaxation labeling processes,
    Proc. ICNN'94-IEEE Int. Conf. on Neural Networks,
    Orlando, Florida, 1994, pp. 1006-1011.
    IEEE Computer Society Press.

14. G. Castellano, A. M. Fanelli, and M. Pelillo,
    Pruning in recurrent neural networks,
    Proc. ICANN'94-Int. Conf. on Artificial Neural Networks,
    Sorrento, Italy, 1994, pp. 451-454.
    Springer-Verlag, Berlin.

15. M. Pelillo and A. Maffione,
    Using simulated annealing to train relaxation labeling processes,
    Proc. ICANN'94-Int. Conf. on Artificial Neural Networks,
    Sorrento, Italy, 1994, pp. 250-253.
    Springer-Verlag, Berlin.

16. M. Pelillo,
    Relaxation labeling networks that solve the maximum clique problem,
    Proc. ANN'95-4th IEE Int. Conf. on Artificial Neural Networks,
    Cambridge, England, 1995, pp. 166-170.

17. M. Pelillo and A. M. Fanelli,
    An asymmetric associative memory model based on relaxation labeling processes,
    Proc ESANN'95-Europ. Symp. on Artificial Neural Networks,
    Brussels, Belgium, 1995, pp. 223-228.

18. M. Pelillo,
    A relaxation algorithm for estimating the domain of validity of feedforward neural networks,
    Proc. ICANN'95-Int. Conf. on Artificial Neural Networks,
    Paris, France, 1995, vol. 2, pp. 443-448.

19. M. Pelillo, F. Abbattista, and A. Maffione,
    Teaching relaxation labeling processes using genetic algorithms,
    Artificial Neural Nets and Genetic Algorithms,
    D. W. Pearson, N. C. Steele, and R. F. Albrecht (Eds.),
    Springer-Verlag, Wien, 1995, pp. 57-60.

20. M. Pelillo,
    Clique finding relaxation labeling networks,
    Recent Developments in Computer Vision,
    S. Z. Li, D. P. Mital, E. K. Teoh, and H. Wang (Eds.),
    (Lecture Notes in Computer Science, Vol. 1035).
    Springer-Verlag, Berlin, 1996, pp. 343-352.
    (Invited paper)

21. M. Pelillo and I. M. Bomze,
    Parallelizable evolutionary dynamics principles for the maximum clique problem,
    Parallel Problem Solving from Nature-PPSN IV,
    H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel (Eds.),
    (Lecture Notes in Computer Science, Vol. 1141).
    Springer-Verlag, Berlin, 1996, pp. 676-685.

22. E. R. Hancock and M. Pelillo,
    An analysis of the exponential correlation associative memory,
    Proc. ICPR'96-13th Int. Conf. on Pattern Recognition,
    Vienna, Austria, 1996, vol. IV, pp. 291-295.
    IEEE Computer Society Press. (ps.gz)

23. M. Pelillo and A. M. Fanelli,
    Autoassociative learning in relaxation labeling networks,
    Proc. ICPR'96-13th Int. Conf. on Pattern Recognition,
    Vienna, Austria, 1996, vol. IV, pp. 105-110.
    IEEE Computer Society Press. (ps.gz)

24. I. M. Bomze, M. Pelillo, and R. Giacomini,
    Evolutionary approach to the maximum clique problem: Empirical evidence on a larger scale,
    Developments in Global Optimization,
    I. M. Bomze, T. Csendes, R. Horst, and P. M. Pardalos (Eds.),
    Kluwer Academic Publishers, Dordrecht, The Netherlands, 1997, pp. 95-108.

25. E. R. Hancock and M. Pelillo,
    A Bayesian framework for associative memories,
    Neural Nets WIRN Vietri-96,
    M. Marinaro and R. Tagliaferri (Eds.),
    Springer-Verlag, London, 1997, pp. 125-131.

26. M. Pelillo, K. Siddiqi, and S. W. Zucker,
    Matching hierachical structures using association graphs,
    Computer Vision-ECCV'98,
    H. Burkhardt and B. Neumann (Eds.),
    (Lecture Notes in Computer Science, vol. 1407),
    Springer-Verlag, Berlin, 1998, pp. 3-16.

27. M. Pelillo,
    A unifying framework for relational structure matching,
    Proc. ICPR'98-14th Int. Conf. on Pattern Recognition,
    Brisbane, Australia, 1998, pp. 1316-1319.
    IEEE Computer Society Press.

28. M. Pelillo,
    Replicator equations, maximal cliques, and graph isomorphism,
    Advances in Neural Information Processing Systems 11,
    M. S. Kearns, S. A. Solla, and D. A. Cohn (Eds.),
    MIT Press, Cambridge, MA, 1999, pp. 550-556. (ps.gz)

29. A. Torsello and M. Pelillo,
    Continuous-time relaxation labeling processes,
    Energy Minimization Methods in Computer Vision and Pattern Recognition-EMMCVPR'99,
    E. R. Hancock and M. Pelillo (Eds.),
    (Lecture Notes in Computer Science, vol. 1654),
    Springer-Verlag, Berlin, 1999, pp. 253-268.

30. M. Pelillo, K. Siddiqi, and S. W. Zucker,
    Attributed tree matching and maximum weight cliques,
    Proc. ICIAP'99-10th Int. Conf. on Image Analysis and Processing,
    IEEE Computer Society Press, 1999, pp. 1154-1159.

31. I. M. Bomze, M. Budinich, M. Pelillo, and C. Rossi,
    A new ``annealed'' heuristic for the maximum clique problem,
    Approximation and Complexity in Numerical Optimization: Continuous and Discrete Problems,
    P. M. Pardalos (Ed.),
    Kluwer Academic Publishers, Boston, MA, 2000, pp. 78-95.

32. M. Pelillo, K. Siddiqi, and S. W. Zucker,
    Continuous-based heuristics for graph and tree isomorphisms, with application to computer vision,
    Approximation and Complexity in Numerical Optimization: Continuous and Discrete Problems,
    P. M. Pardalos (Ed.),
    Kluwer Academic Publishers, Boston, MA, 2000, pp. 422-445.
    (Invited paper)

33. A. Jagota, M. Pelillo, and A. Rangarajan,
    A new deterministic annealing algorithm for maximum clique,
    Proc. IJCNN'2000-Int. J. Conf. on Neural Networks,
    IEEE Computer Society Press, 2000, Vol. VI, pp. 505-508.

34. M. Bartoli, M. Pelillo, K. Siddiqi, and S. W. Zucker,
    Attributed tree homomorphism using association graphs,
    Proc. ICPR'2000-15th Int. Conf. on Pattern Recognition,
    IEEE Computer Society Press, 2000, Vol. 2, pp. 133-136.

35. M. Pelillo,
    Matching free trees using association graphs,
    Computer Vision: 6th Computer Vision Winter Workshop,
    B. Likar (Ed.),
    Bled, Slovenia, 2001, pp. 276-285.

36. M. Pelillo,
    Evolutionary game dynamics in combinatorial optimization: An overview,
    Applications of Evolutionary Computing,
    E. J. W. Boers et al. (Eds.),
    (Lecture Notes in Computer Science, vol. 2037),
    Springer, Berlin, 2001, pp. 182-192.

37. M. Pelillo, K. Siddiqi, and S. W. Zucker,
    Many-to-many matching of attributed trees using association graphs and game dynamics,
    Visual Form 2001,
    C. Arcelli, L. P. Cordella, and G. Sanniti di Baja (Eds.),
    (Lecture Notes in Computer Science, vol. 2059),
    Springer, Berlin, 2001, pp. 583-593.

38. M. Pelillo,
    Matching free trees with replicator equations,
    Advances in Neural Information Processing Systems 14,
    T. G. Dietterich, S. Becker, and Z. Ghahramani (Eds.),
    MIT Press, Cambridge, MA, 2002, pp. 865-872.

39. M. Pavan and M. Pelillo,
    A new graph-theoretic approach to clustering and segmentation,
    Proc. CVPR 2003 - IEEE Conf. on Computer Vision and Pattern Recognition,
    IEEE Computer Society Press, 2003, Vol. I, pp. 145-152.

40. M. Pelillo,
    Annealed imitation: Fast dynamics for maximum clique,
    Proc. IJCNN 2003 - IEEE International Joint Conference on Neural Networks,
    Portland, Oregon, 2003, pp. 55-60.

41. M. Pavan and M. Pelillo,
    Dominant sets and hierarchical clustering,
    Proc. ICCV 2003 - 9th IEEE International Conference on Computer Vision,
    IEEE Computer Society Press, 2003, Vol. I, pp. 362-369.

42. A. Torsello, D. Hidovic, and M. Pelillo,
    A polynomial-time metric for attributed trees.
    Computer Vision — ECCV 2004,
    T. Pajdla and J. Matas (Eds.),
    (Lecture Notes in Computer Science, Vol. 3024)
    Springer, Berlin, pp. 414–427, 2004.

43. A. Torsello, D. Hidovic, and M. Pelillo,
    Four metrics for efficiently comparing attributed trees.
    Proc. ICPR’04 - 17th International Conference on Pattern Recognition.
    Cambridge, UK, 2004, Vol. 2, pp. 467–470.

44. M. Pavan and M. Pelillo,
    Effcient out-of-sample extension of dominant-set clusters.
    Advances in Neural Information Processing Systems 17,
    L. K. Saul, Y. Weiss, and L. Bottou (Eds.),
    MIT Press, Cambridge, MA, 2005, pp. 1057-1064.

45. A. Torsello, M. Pavan, and M. Pelillo.
    Spatio-temporal segmentation using dominant sets.
    Energy Minimization Methods in Computer Vision and Pattern Recognition-EMMCVPR'05,
    A. Rangarajan, B. Vemuri, and A. L. Yuille (Eds.),
    (Lecture Notes in Computer Science, vol. 3757),
    Springer-Verlag, Berlin, 2005, pp. 301-315.

46. A. Torsello, S. Rota Bulo', and M. Pelillo.
    Grouping with asymmetric affinities: A game-theoretic perspective.
    Proc. CVPR 2006 - IEEE International Conference on Computer Vision and Pattern Recognition,
    New York, NY, USA, June 2006, vol. 1, pp. 292-299.

47. S. Rota Bulo', A. Albarelli, A. Torsello, and M. Pelillo.
    A hypergraph-based approach to affine parameters estimation.
    Proc. ICPR 2008 - International Conference on Pattern Recognition,
    Tampa, FL, December 2008 (accepted for oral presentation).

48. A. Torsello, S. Rota Bulo', and M. Pelillo.
    Beyond partitions: Allowing overlapping groups in pairwise clustering.
    Proc. ICPR 2008 - International Conference on Pattern Recognition,
    Tampa, FL, December 2008 (accepted for oral presentation).

49. A. Torsello and M. Pelillo.
    Hierarchical pairwise segmentation using dominant sets and anisotropic diffusion kernels.
    Energy Minimization Methods in Computer Vision and Pattern Recognition-EMMCVPR'09,
    Bonn, Germany, August 2009 (accepted for oral presentation).

50. A. Albarelli, A. Torsello, S. Rota Bulo', and M. Pelillo.
    Matching as a non-cooperative game.
    Proc. ICCV 2009 - 12th IEEE International Conference on Computer Vision,
    Kyoto, Japan, October 2009 (accepted for presentation).

51. S. Rota Bulo', and M. Pelillo.
    A game-theoretic approach to hypergraph clustering.
    Proc. NIPS 2009: Neural Information Processing Systems,
    Vancouver, Canada, December 2009 (accepted for presentation).