Technical Committee 14
Signal Analysis for Machine Intelligence

Education

 

Books

Links to Course Material

Survey Papers

Papers

Special Issues

Tutorials

 

Books

  • C. M. Bishop Pattern Recognition and Machine Learning, Springer 2006.
  • C. M. Bishop Neural Networks for Pattern Recognition, Oxford University Press, 1995.
  • L. Devroy, L. Gyorfi, G. Lugosi A Probabilistic Theory of Pattern Recognition, Springer, 1996.
  • R. O. Duda, H. Hart, D. Stork Pattern Classification and Scene Analysis, John Wiley, 2001.
  • K. Fukunaga Introduction to Statistical Pattern Recogntion, 2nd Edition, Academic Press, 1990.
  • B. D. Ripley Pattern Recognition and Neural Networks, Cambridge University Press, 1996.
  • S. Theodoridis, K. Koutroumbas Pattern Recognition, 4nd Edition, Academic Press, 2008.
  • S. Theodoridis, A. Pikrakis, K. Koutroumbas, D. Cavouras An Introduction to Pattern Recognition: A MATLAB Approach, Avademic Press, 2010.
  • J. Shawe-Taylor, N. Christianini Kernel Methods for Pattern Recognition, Cambridge University Press, 2004.
  • A. Webb Statistical Pattern Recognition, 2nd Edition, John Wiley, 2002.
  • O.Chapelle, B. Scholkopf, A. Zien Semisupervised Learning, MIT Press, 2006.
  • T. Hastie, R. Tibshirani, J. Friedman The Elements of Statistical Learning, Springer, 2001.
  • S. Marsland Machine Learning: An Algorithmic Perspective , CRC Press, 2009.
  • V. Vapnik Statistical Learning Theory, John Wiley, 1998.
  • V. Vapnik The Nature of Statistical Learning, 2nd Edition, Springer, 2000.
  • S. Haykin Neural Networks, 2nd Edition, Prentice Hall, 1999.
  • C.E. Rasmusen, C. Williams Gaussian Processes for Machine Learning, MIT Press, 2006.
  • B. Scholkopf, A. J. Smola Learning with Kernels, MIT Press, 2002.
  • Daphne Koller , Nier Friedman Graphical Models: Principles and Techniques, MIT Press, 2009.
  • J. Pearl Probabilistic Reasoning in Intelligence Systems, Morgan Kaufmann, 1988.
  • R. D. Neapolitan Learning Bayesian Networks, Prentice Hall, 2004.
  • John Lee, M. Verleysen Nonlinear Dimensionality Techniques, Springer, 2007.
  • T. Adali and S. Haykin, Adaptive Signal Processing: Next Generation Solutions, Wiley, 2010
  • L. B. Almeida, Nonlinear Source Separation, Synthesis Lectures on Signal Processing, Morgan and Claypool Publishers, 2006.
  • J. R. Deller, J. H. L. Hansen, J. G. Proakis Discrete-Time Processing of Speech Signals, John Wiley, 2000.
  • H. Liu, H. Motoda, Computational Methods for Feature Selection, CRC, 2008.

Links to Course Material 

Survey Papers

  • F. Perronnin, Z. Akata, Z. Harchaoui, and C. Schmid. Towards good practice in large-scale learning for image classification. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on (pp. 3482-3489). IEEE, 2012.
  • K. Chatfield, V. Lempitsky, A. Vedaldi, and A. Zisserman. The devil is in the details: an evaluation of recent feature encoding methods. British Machine Vision Conference (BMVC), Dundee, 2011. paper
  • R. Xu, D. Wunsch. Survey of clustering algorithms. IEEE Transactions on Neural Networks. Vol. 16(3), pp. 645-678, 2005.
  • A.K. Jain, P.W. Duin, and J. Mao. Statistical pattern recognition: A review. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 22(1), pp. 4-37, 2000.
  • M. L. Kherfi, D. Ziou, and A. Bernardi. Image Retrieval from the World Wide Web: Issues, Techniques, and Systems. ACM Computing Surveys, Vol.36(1), March 2004.
  • A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 12, pp. 1349--1380, 2000.
  • Y. Wang, J.C. Huang. Multimedia content analysis. IEEE Signal Processing Magazine, Vol. 17(6), pp. 12-36, 2000.
  • E. Wold, T. Blum, D. Keislar, and J. Wheaton. Content based classification, search, and retrieval of audio. IEEE Multimedia Magazine, Vol. 22, pp.27-36, 1996.
  • H. Zhang, Q. Tian. Digital video analysis and recognition for content-based access. ACM Computing Surveys, Vol.27, N.4, December 1995.

Papers

  • S. Nikolopoulos, S. Zafeiriou, I. Patras, I. Kompatsiaris. High order pLSA for indexing tagged images. Signal Processing, Volume 93, Issue 8, pp. 2212-2228, August 2013.
  • C. Arteta, V. Lempitsky, A. Noble, and A. Zisserman. Learning to Detect Partially Overlapping Instances. IEEE Computer Vision and Pattern Recognition (CVPR), Portland OR, 2013 (to appear).
  • A. Babenko and V. Lempitsky. The Inverted Multi-Index. IEEE Computer Vision and Pattern Recognition (CVPR), Providence RI, 2012.
  • K.G. Derpanis and R.P. Wildes. Spacetime texture representation and recognition based on a spatiotemporal orientation analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 34 (6), 1193-1205, 2012.
  • H. Jegou, M. Douze, M., C. Schmid. Product quantization for nearest neighbor search. Pattern Analysis and Machine Intelligence. IEEE Transactions on, 33(1), 117-128, 2011
  • S. Papadopoulos, C. Zigkolis, Y. Kompatsiaris, A. Vakali. Cluster-based Landmark and Event Detection on Tagged Photo Collections. In IEEE Multimedia Magazine 18(1), pp. 52-63, 2011
  • Y. Weiss, A. Torralba, R. Fergus. Spectral hashing. In Advances in neural information processing systems (pp. 1753-1760), 2008.

Special Issues

  • Meng Wang, Xinbo Gao, Yi Yang and Caifeng Shan, Special Issue on "Indexing of Large-scale Multimedia Signals", August 2013.
  • IEEE Signal Processing Magazine: Special Issue on Semantic Retrieval of Multimedia, Vol. 23(2), 2006.
  • IEEE Signal Processing Magazine: Special Issue on Knowledge Based Systems for Adaptive Radar, Vol. 23(1), 2006.
  • IEEE Signal Processing Magazine: Special Issue on Speech Technology and Systems in Human-Machine Communication, Vol. 22(5), 2005.
  • IEEE Signal Processing magazine: Special Issue on Universal Multimedia Access, Vol. 20(2), 2003.
  • IEEE Signal Processing Magazine: Special Issue on Collaborative Information Processing, Vol. 19(2), 2002.
  • IEEE Signal Processing Magazine: Special Issue on Audio-Visual Processing, Vol. 17(6), 2000.

Tutorials

  • Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods. Arenas-Garcia, J. ; Petersen, K. ; Camps-Valls, G. ; Hansen, L.K.