Speaker: Professor Moncef Gabbouj
Title: Machine Learning and Optimization Tools for Multimedia Data Analytics and Retrieval
Time: 14:30-15:30, April 12th, 2023 (Wednesday)
Website: Teams Link
Abstract:
Multimedia Data analytics is the core engine in modern decision-making environments. In this talk, we discuss modern signal processing, machine learning, pattern recognition and optimization tools recently developed and used in multimedia data analytics with a special emphasis on multimedia data retrieval. Classification and search in large media repositories will be the main targeted applications. 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 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 multi-dimensional fitness function. Two novel techniques, which extend the basic PSO algorithm, are presented. In a multidimensional search space where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. The resulting MD-PSO plays a key role in developing machine learning tools for data classification and feature synthesis. 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. The talk will also review and propose novel deep learning paradigms that has the potential to revolutionize media retrieval.
Personal Introduction:
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 a Professor of Information Technology at the Department of Computing Sciences, Tampere University, Tampere, Finland. He was Academy of Finland Professor during 2011-2015. He held several visiting professorships at different universities. Dr. Gabbouj is currently the Finland Site Director of the NSF IUCRC funded Center for Visual and Decision Informatics. His research interests include Big Data analytics, multimedia content-based analysis, indexing and retrieval, artificial intelligence, machine learning, pattern recognition, nonlinear signal and image processing and analysis, voice conversion, and video processing and coding. Dr. Gabbouj is a Fellow of the IEEE and Asia-Pacific Artificial Intelligence Association. He is member of the Academia Europaea, the Finnish Academy of Science and Letters and the Finnish Academy of Engineering Sciences. He is the past Chairman of the IEEE CAS TC on DSP. He is a member of the IEEE Signal Processing Society Fourier Award Committee and Vice-Chair of the IEEE Computer Science Society Harry Goode Award Committee. He served as Distinguished Lecturer for the IEEE CASS. He served as associate editor and guest editor of many IEEE, and international journals as well as General Chair of IEEE SPS and CAS Flagship conferences, ICIP and ISCAS as well as ICME 2021. Dr. Gabbouj was the recipient of the 2017 Finnish Cultural Foundation for Art and Science Award, the 2015 TUT Foundation Grand Award, the 2012 Nokia Foundation Visiting Professor Award, the 2005 Nokia Foundation Recognition Award, and several Best Paper Awards. Dr. Gabbouj is the Finland Site Director of the NSF IUCRC funded Center for Visual and Decision Informatics (CVDI) and led the Artificial Intelligence Research Task Force of the Ministry of Economic Affairs and Employment funded Research Alliance on Autonomous Systems (RAAS). He published two books and over 850 journal and conference papers and supervised 54 doctoral and 76 Master theses.
[Editor:Xiaohan Liu]