Aleksandr Algasov and Heikki Kälviäinen presents Animal Re-Identification Models to Address Small Datasets + Automated Plankton Image Recognition

The seminar will have two parts:
1. Aleksandr Algasov: On Combining Animal Re-Identification Models to Address
Small Datasets
2. Heikki Kälviäinen: Automated Plankton Image Recognition

Abstract 1:
Recent advancements in the automatic re-identification of animal individuals
from images have opened up new possibilities for studying wildlife through
camera traps and citizen science projects. Existing methods leverage distinct
and permanent visual body markings, such as fur patterns or scars, and
typically
employ one of two approaches: local features or end-to-end learning. The
end-to-end learning-based methods outperform local feature-based methods given
a
sufficient amount of good-quality training data, but the challenge of gathering
such datasets for wildlife animals means that local feature-based methods
remain
a more practical approach for many species. In this study, we aim to achieve
two
goals: (1) to obtain a better understanding of the impact of training-set size
on animal re-identification, and (2) to explore ways to combine various methods
to leverage the advantages of their approaches for re-identification. In the
work, we conduct comprehensive experiments across six different methods and six
animal species with various training set sizes. Furthermore, we propose a
simple
yet effective combination strategy and show that a properly selected method
combinations outperform the individual methods with both small and large
training sets up to 30%. Additionally, the proposed combination strategy offers
a generalizable framework to improve accuracy across species and address the
challenges posed by small datasets, which are common in ecological research.
This work lays the foundation for more robust and accessible tools to support
wildlife conservation, population monitoring, and behavioral studies.

Abstract 2:
The presentation considers computer vision and machine learning in plankton
image recognition, especially from a point of view of applications. Digital
image processing and analysis with machine learning methods could enable
efficient solutions for various areas of useful data-centric engineering
applications. Challenges with imbalance of datasets, domain adaptation, active
learning, open set classification, and metric learning of similarities are
considered and related examples are given in plankton recognition, based on the
research of LUT Computer Vision and Pattern Recognition Laboratory (CVPRL).


Biographies:
rof. Heikki Kälviäinen is a Professor of Computer Science and Engineering in
LUT University, Finland, and works in the Computer Vision and Pattern
Recognition Laboratory (CVPRL). Prof. Kälviäinen’s research interests
include computer vision, pattern recognition, machine learning, and
applications
of digital image processing and analysis: 30 doctoral theses and 173 master's
theses supervised, 37 doctoral dissertation evaluated, more than 230
peer-reviewed scientific articles, and research projects of 6,3 MEUR in total.
Besides LUT, Prof. Kälviäinen has worked more than six years in total in the
following universities: Brno University of Technology, Czech Technical
University, Monash University, Rensselaer Polytechnic Institute, and University
of Surrey.

M.Sc. Aleksandr Algasov is a Ph.D. student in LUT University, Finland, and
works
in the Computer Vision and Pattern Recognition Laboratory (CVPRL). His research
interests include animal biometrics, especially re-identification of individual
animals. Mr. Algasov is a double degree doctoral student of LUT and Brno
University of Technology in Czech Republic.