J. Aggarwal and M. Ryoo, Human activity analysis, ACM Computing Surveys, vol.43, issue.3, pp.1-1643, 2011.
DOI : 10.1145/1922649.1922653

A. Acierno, M. Leone, A. Saggese, and M. Vento, A system for storing and retrieving huge amount of trajectory data, allowing spatiotemporal dynamic queries, Proceedings of the " IEEE Conference on Intelligent Transportation Systems (ITSC), pp.2012-989

G. Acampora, P. Foggia, A. Saggese, and M. Vento, Combining Neural Networks and Fuzzy Systems for Human Behavior Understanding, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, pp.2012-88
DOI : 10.1109/AVSS.2012.25

H. Dee and D. Hogg, Detecting inexplicable behaviour The British Machine Vision Association, Proceedings of the British Machine Vision Conference, pp.477-486, 2004.
DOI : 10.5244/c.18.50

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.422.782

V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection, ACM Computing Surveys, vol.41, issue.3, pp.1-1558, 2009.
DOI : 10.1145/1541880.1541882

Y. Zhou, S. Yan, and T. Huang, Detecting Anomaly in Videos from Trajectory Similarity Analysis, Multimedia and Expo, 2007 IEEE International Conference on, pp.1087-1090, 2007.
DOI : 10.1109/ICME.2007.4284843

B. Morris and M. Trivedi, Trajectory learning for activity understanding: Unsupervised, multilevel, and long-term adaptive approach Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.33, issue.11, pp.2287-2301, 2011.

R. , D. Lascio, P. Foggia, A. Saggese, and M. Vento, Tracking interacting objects in complex situations by using contextual reasoning, VISAPP, pp.104-113, 2012.

L. Brun and A. Trémeau, Digital Color Imaging Handbook, ser. Electrical and Applied Signal Processing, pp.589-637, 2002.

J. P. Braquelaire and L. Brun, Comparison and optimization of methods of color image quantization, IEEE Transactions on Image Processing, vol.6, issue.7, pp.1048-1052, 1997.
DOI : 10.1109/83.597280

S. J. Wan, S. K. Wong, and P. Prusinkiewicz, An algorithm for multidimensional data clustering, ACM Transactions on Mathematical Software, vol.14, issue.2, pp.153-162, 1988.
DOI : 10.1145/45054.45056

H. Shimodaira, K. Ichi-noma, M. Nakai, and S. Sagayama, Dynamic time-alignment kernel in support vector machine, Advances in Neural Information Processing Systems (NIPS2002), pp.921-928, 2002.

H. Saigo, J. Vert, N. Ueda, and T. Akutsu, Protein homology detection using string alignment kernels, Bioinformatics, vol.20, issue.11, pp.1682-1689, 2004.
DOI : 10.1093/bioinformatics/bth141

URL : https://hal.archives-ouvertes.fr/hal-00433587

M. Neuhaus and H. Bunke, Edit distance-based kernel functions for structural pattern classification, Pattern Recognition, vol.39, issue.10, pp.1852-1863, 2006.
DOI : 10.1016/j.patcog.2006.04.012

M. Cuturi, Fast global alignment kernels, Proceedings of the 28th International Conference on Machine Learning (ICML-11), ser. ICML '11, pp.929-936, 2011.

J. Lee, J. Han, and K. Whang, Trajectory clustering, Proceedings of the 2007 ACM SIGMOD international conference on Management of data , SIGMOD '07, pp.593-604, 2007.
DOI : 10.1145/1247480.1247546

P. Foggia, G. Percannella, C. Sansone, and M. Vento, Assessing the Performance of a Graph-Based Clustering Algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) LNCS, vol.4538, pp.215-227, 2007.
DOI : 10.1007/978-3-540-72903-7_20

S. Schaeffer, Graph clustering, Computer Science Review, vol.1, issue.1, pp.27-64, 2007.
DOI : 10.1016/j.cosrev.2007.05.001

P. Foggia, G. Percannella, C. Sansone, and M. Vento, A GRAPH-BASED ALGORITHM FOR CLUSTER DETECTION, International Journal of Pattern Recognition and Artificial Intelligence, vol.22, issue.05, pp.843-860, 2008.
DOI : 10.1142/S0218001408006557

I. S. Dhillon, Y. Guan, and B. Kulis, Kernel k-means, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.551-556, 2004.
DOI : 10.1145/1014052.1014118

G. F. Tzortzis and A. C. Likas, The Global Kernel <formula formulatype="inline"> <tex Notation="TeX">$k$</tex></formula>-Means Algorithm for Clustering in Feature Space, IEEE Transactions on Neural Networks, vol.20, issue.7, pp.1181-1194, 2009.
DOI : 10.1109/TNN.2009.2019722

B. Schölkopf, A. Smola, and K. Müller, Nonlinear Component Analysis as a Kernel Eigenvalue Problem, Neural Computation, vol.20, issue.5, pp.1299-1319, 1998.
DOI : 10.1007/BF02281970

X. Wang, K. T. Ma, G. Ng, and W. E. Grimson, Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models, International Journal of Computer Vision, vol.67, issue.3, pp.287-312, 2011.
DOI : 10.1007/s11263-011-0459-6

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.488.1141

L. Hubert and J. Schultz, QUADRATIC ASSIGNMENT AS A GENERAL DATA ANALYSIS STRATEGY, British Journal of Mathematical and Statistical Psychology, vol.29, issue.2, pp.190-241, 1976.
DOI : 10.1111/j.2044-8317.1976.tb00714.x