TY - GEN
T1 - A Method for Selecting a Representative Image of a Dataset Based on the Singular Value Decomposition
AU - Soto-Quiros, Pablo
AU - Figueroa-Mata, Geovanni
AU - Zamora-Villalobos, Nelson
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, we present a novel approach for obtaining a representative image from a dataset \mathcal{M} based on the singular value decomposition (SVD). The proposed method consists of two phases: The first phase involves calculating a theoretical representative image I{T}, which is obtained using some measure of central tendency. This image I{T} may not necessarily represent an image from the dataset \mathcal{M}. Therefore, in the second phase, we calculate the practical representative image IP\in\mathcal{M} by utilizing I{T} and the image subspace generated by \mathcal{M} through an orthonormal basis, which spans the entire subspace \mathcal{M}. This basis is obtained using the SVD of the matrix formed by vectorizing the images in \mathcal{M}. Finally, we conduct simulations of the proposed method and compare it with existing methods in the literature. The advantages of our approach are analyzed and demonstrated through numerical experiments.
AB - In this paper, we present a novel approach for obtaining a representative image from a dataset \mathcal{M} based on the singular value decomposition (SVD). The proposed method consists of two phases: The first phase involves calculating a theoretical representative image I{T}, which is obtained using some measure of central tendency. This image I{T} may not necessarily represent an image from the dataset \mathcal{M}. Therefore, in the second phase, we calculate the practical representative image IP\in\mathcal{M} by utilizing I{T} and the image subspace generated by \mathcal{M} through an orthonormal basis, which spans the entire subspace \mathcal{M}. This basis is obtained using the SVD of the matrix formed by vectorizing the images in \mathcal{M}. Finally, we conduct simulations of the proposed method and compare it with existing methods in the literature. The advantages of our approach are analyzed and demonstrated through numerical experiments.
KW - Measures of central tendency
KW - Orthonormal Basis
KW - Representative image
KW - Singular Value Decomposition
UR - http://www.scopus.com/inward/record.url?scp=85184352495&partnerID=8YFLogxK
U2 - 10.1109/BIP60195.2023.10379342
DO - 10.1109/BIP60195.2023.10379342
M3 - Contribución a la conferencia
AN - SCOPUS:85184352495
T3 - 5th IEEE International Conference on BioInspired Processing, BIP 2023
BT - 5th IEEE International Conference on BioInspired Processing, BIP 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on BioInspired Processing, BIP 2023
Y2 - 28 November 2023 through 30 November 2023
ER -