activity
20162021
most citedSemi-supervised Superpixel-based Multi-Feature Graph Learning for Hyperspectral Image Data

19 citations · 19 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CV2021★ 19 cited

Semi-supervised Superpixel-based Multi-Feature Graph Learning for Hyperspectral Image Data

Madeleine Kotzagiannidis, Carola-Bibiane Schönlieb

Graphs naturally lend themselves to model the complexities of Hyperspectral Image (HSI) data as well as to serve as semi-supervised classifiers by propagating given labels among ne…

cs.DM2018

Analysis vs Synthesis with Structure - An Investigation of Union of Subspace Models on Graphs

Madeleine S. Kotzagiannidis, Mike E. Davies

We consider the problem of characterizing the `duality gap' between sparse synthesis- and cosparse analysis-driven signal models through the lens of spectral graph theory, in an ef…

eess.SP2018

Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs

Madeleine S. Kotzagiannidis, Mike E. Davies

In this work, we present a theoretical study of signals with sparse representations in the vertex domain of a graph, which is primarily motivated by the discrepancy arising from re…

cs.DM2016

Sampling and Reconstruction of Sparse Signals on Circulant Graphs - An Introduction to Graph-FRI

Madeleine S. Kotzagiannidis, Pier Luigi Dragotti

With the objective of employing graphs toward a more generalized theory of signal processing, we present a novel sampling framework for (wavelet-)sparse signals defined on circulan…

cs.DM2016

Splines and Wavelets on Circulant Graphs

Madeleine S. Kotzagiannidis, Pier Luigi Dragotti

We present novel families of wavelets and associated filterbanks for the analysis and representation of functions defined on circulant graphs. In this work, we leverage the inheren…