Non-Separable Multi-Dimensional Network Flows for Visual Computing

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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Flows in networks (or graphs) play a significant role in numerous computer vision tasks. The scalar-valued edges in these graphs often lead to a loss of information and thereby to limitations in terms of expressiveness. For example, oftentimes highdimensional data (e.g. feature descriptors) are mapped to a single scalar value (e.g. the similarity between two feature descriptors). To overcome this limitation, we propose a novel formalism for non-separable multi-dimensional network flows. By doing so, we enable an automatic and adaptive feature selection strategy - since the flow is defined on a per-dimension basis, the maximizing flow automatically chooses the best matching feature dimensions. As a proof of concept, we apply our formalism to the multi-object tracking problem and demonstrate that our approach outperforms scalar formulations on the MOT16 benchmark in terms of robustness to noise.
Description

CCS Concepts: Theory of computation -> Design and analysis of algorithms; Theory and algorithms for application domains

        
@inproceedings{
10.2312:egp.20231028
, booktitle = {
Eurographics 2023 - Posters
}, editor = {
Singh, Gurprit
and
Chu, Mengyu (Rachel)
}, title = {{
Non-Separable Multi-Dimensional Network Flows for Visual Computing
}}, author = {
Ehm, Viktoria
and
Cremers, Daniel
and
Bernard, Florian
}, year = {
2023
}, publisher = {
The Eurographics Association
}, ISSN = {
1017-4656
}, ISBN = {
978-3-03868-211-0
}, DOI = {
10.2312/egp.20231028
} }
Citation