Machine Learning and Particle Swarm Optimization Tools for Searching in Big Databases

The talk deals with a new paradigm for multimedia search based on content. We present an alternative approach to classical search engines for information retrieval, which can be used for Big and generic multimedia repositories. We introduce an incremental evolution scheme within a collective network of (evolutionary) binary classifier (CNBC) framework. The proposed framework addresses the problems of feature/class scalability and achieves high classification and content-based retrieval performances over dynamic image repositories. The secret behind the success of CNBC is a novel design to implement the backbone of CNBC, namely the binary classifier. This is a special neural network, which is optimally designed using the recently developed evolutionary optimization algorithm called multi-dimensional particle swarm optimization. Particle swarm optimization (PSO) is population based stochastic search and optimization process, which was introduced in 1995 by Kennedy and Eberhart. The goal is to converge to the global optimum of some multidimensional fitness function. Two novel techniques, which extend the basic PSO algorithm, are presented. The first algorithm called multi-dimensional PSO (M-D PSO) deals with problems in which the dimension of the solution space is not known a priori. M-D PSO solves such a problem by introducing two interleaved PSO iteration processes, a positional PSO followed by a dimensional PSO in which the dimension of a particle is allowed to vary. In a multidimensional search space where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. Most content-based multimedia search engines available today rely heavily on low-level features. However, such features extracted automatically usually lack discrimination power needed for accurate description of the image content and may lead to poor retrieval performance. To address this problem, we propose an evolutionary feature synthesis technique, which seeks for the optimal linear and non-linear operations over optimally selected features so as to synthesize highly discriminative features. The optimality therein is sought through MD-PSO. The synthesized features are applied over only a minority of the original feature vectors and exhibit a major discrimination power between different classes and extensive CBIR experiments show that a significant performance improvement can be achieved.


Moncef Gabbouj received his BS degree in electrical engineering in 1985 from Oklahoma State University, Stillwater, and his MS and PhD degrees in electrical engineering from Purdue University, West Lafayette, Indiana, in 1986 and 1989, respectively.

Dr. Gabbouj is an Academy Professor with the Academy of Finland since January 2011. He held several visiting professorships at different universities, including The Hong Kong University of Science and Technology, Hong Kong (2012-2013), Purdue University, West Lafayette, Indiana, USA (August-December 2011), the University of Southern California (January-June 2012), and the American University of Sharjah, UAE, (2007-2008). He holds a permanent position of Professor at the Department of Signal Processing, Tampere University of Technology, Tampere, Finland, where he leads the Multimedia Research Group. He was Head of the Department during 2002-2007, and served as Senior Research Fellow of the Academy of Finland in 1997-1998 and 2007-2008. His research interests include multimedia content-based analysis, indexing and retrieval, machine learning, nonlinear signal and image processing and analysis, voice conversion, and video processing and coding.

Dr. Gabbouj is a Fellow of the IEEE, a member of the European Academy and the Finnish Academy of Science and Letters. He is the past Chairman of the DSP Technical Committee of the IEEE Circuits and Systems Society and member of the IEEE Fourier Award for Signal Processing Committee. He was Honorary Guest Professor of Jilin University, China (2005-2010). He served as Distinguished Lecturer for the IEEE Circuits and Systems Society in 2004-2005, and Past-Chairman of the IEEE-EURASIP NSIP (Nonlinear Signal and Image Processing) Board. He was chairman of the Algorithm Group of the EC COST 211quat. He served as associate editor of the IEEE Transactions on Image Processing, and was guest editor of Multimedia Tools and Applications, the European journal Applied Signal Processing. He is the past chairman of the IEEE Finland Section, the IEEE Circuits and Systems Society, Technical Committee on Digital Signal Processing, and the IEEE SP/CAS Finland Chapter. He was also (co-)Chairman of BigDataSE 2015, EUVIP 2014, CBMI 2005, WIAMIS 2001 and the TPC Chair of ISCCSP 2012, 2006 and 2004, CBMI 2003, EUSIPCO 2000, NORSIG 1996 and the DSP track co-chair of the 2013, 2012, 2011, and 1996 IEEE ISCAS. He is also member of EURASIP Advisory Board and past member of AdCom. He also served as Publication Chair and Publicity Chair of IEEE ICIP 2005 and IEEE ICASSP 2006, respectively, and the Innovation chair of ICIP 2011. He is a member of IEEE SP and CAS societies. Dr. Gabbouj is member of the Finnish Academy of Science and Letters.

He is Fellow of the IEEE, Distinguished Scientist of ACM, and Senior Member of INNS. He is IEEE Past Vice President for Technical Activities (2016) and has been IEEE Vice President for Technical Activities, IEEE Director, President of the IEEE Computational Intelligence Society, Vice President for Education of the IEEE Biometrics Council, Vice President for Publications of the IEEE Instrumentation and Measurement Society and the IEEE Systems Council, and Vice President for Membership of the IEEE Computational Intelligence Society. He is Editor-in-Chief of the IEEE Systems Journal (2013-17), and has been Associate Editor of the IEEE Transactions on Neural Networks and the IEEE Transactions on Instrumentation and Measurement.

He received the IEEE Instrumentation and Measurement Society Technical Award (2002) for the contributions to the advancement of theory and practice of computational intelligence in measurement systems and industrial applications. He is Honorary Professor at the Obuda University, Budapest, Hungary (since 2014), Guest Professor at Guangdong University of Petrochemical Technology, China (since 2014), and Guest Professor at the Muroran Institute of Technology, Japan (since 2016).
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