4th International Conference

Digital Culture & AudioVisual Challenges

Interdisciplinary Creativity in Arts and Technology

Hybrid - Corfu/Online, May 13-14, 2022

KEYNOTE

Alexandros Iosifidis

Alexandros Iosifidis is a Professor at Aarhus University, Denmark. He leads the Machine Learning & Computational Intelligence group at the Department of Electrical and Computer Engineering, and the Machine Intelligence research area of the University's Centre for Digitalisation, Big Data and Data Analytics (DIGIT). He has contributed to more than thirty R&D projects financed by EU, Finnish, and Danish funding agencies and companies. He has co-authored 90+ articles in international journals and 120+ papers in international conferences/workshops in topics of his expertise. He is an Editor of the Deep Learning for Robot Perception and Cognition book. His work received several awards, including the Academy of Finland Postdoctoral Research Fellowship 2016, the H.C. Ørsted Young Researcher Prize 2018 for contributions to Signal Processing and Machine Learning, and the EURASIP Early Career Award 2021 for contributions to Statistical Machine Learning and Artificial Neural Networks.

Alexandros is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and he served as an Officer of the Finnish IEEE Signal Processing-Circuits and Systems Chapter from 2016 to 2018. He is a member of the Technical Area Committee on Visual Information Processing of the European Association of Signal Processing (EURASIP), and a member of the IEEE Technical Committee on Machine Learning for Signal Processing. He is currently serving as Associate Editor in Chief for the Neurocomputing journal (covering the research area of Neural Networks), as an Area Editor for the Signal Processing: Image Communication journal, and as an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems journal. He contributed to the organization of several international conferences as an Area Chair or Technical Program Committee Chair, including IEEE ICIP (2018-2022), EUSIPCO (2019,2021), and as Publicity co-Chair for IEEE ICME 2021.

Title: Using Machine Learning for Automatic Visual Content Analysis in Photographic Studies

Abstract:

Quantitative research of visual studies in humanities and social sciences is traditionally conducted on a few hundred images at most due to the difficulty of carefully and manually analyzing various aspects of photographs. Therefore, the scope of visual research has so far been quite limited in terms of the number of analyzed images. However, recent and emerging machine learning methods now allow us to automatically extract a plethora of semantic information from photographs, such as the types of objects, animals, scenes, and even the ages and emotions of people. Such advanced tools can replace the manual annotation process in visual studies and offer the researchers a much deeper insight into the material as these tools enable not only a wider variety of information to be extracted, but also a thousandfold greater number of photographs to be analyzed than has been possible before.

In this talk, I will give an introduction of our recent work on machine learning-based search and analysis of large image archives. We proposed an Automatic Visual Content Analysis model (AVCA for short) which can be employed in several domains in humanities and social sciences, and it can be adjusted and scaled accordingly into various research settings. I will also provide a short introduction to our newly developed software toolkit for automatic information extraction and advanced search operations in large image archives. This software tool, as a realization of AVCA, is publicly available, which will hopefully encourage researchers of humanistic fields to use automated tools in their studies to enable large-scale analyses.