Robustness Aspects of Optimized Centroids

0580817 - ÚI 2024 RIV CH eng C - Conference Paper (international conference)
Kalina, Jan - Janáček, Patrik
Robustness Aspects of Optimized Centroids.
Classification and Data Science in the Digital Age. Cham: Springer, 2023 - (Brito, P.; Dias, J.; Lausen, B.; Montanari, A.; Nugent, R.), s. 193-201. Studies in Classification, Data Analysis, and Knowledge Organization. ISBN 978-3-031-09033-2.
[IFCS 2022: The Conference of the International Federation of Classification Societies /17./. Porto (PT), 19.07.2022-23.07.2022]
R&D Projects: GA ČR(CZ) GA22-02067S
Institutional support: RVO:67985807
Keywords : Image processing * Optimized centroids * Robustness * Sparsity * Low-energy replacements
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://doi.org/10.1007/978-3-031-09034-9_22

Centroids are often used for object localization tasks, supervised segmentation in medical image analysis, or classification in other specific tasks. This paper starts by contributing to the theory of centroids by evaluating the effect of modified illumination on the weighted correlation coefficient. Further, robustness of various centroid-based tools is investigated in experiments related to mouth localization in non-standardized facial images or classification of high-dimensional data in a matched pairs design. The most robust results are obtained if the sparse centroid-based method for supervised learning is accompanied with an intrinsic variable selection. Robustness, sparsity, and energy-efficient computation turn out not to contradict the requirement on the optimal performance of the centroids.
Permanent Link: https://hdl.handle.net/11104/0349582