Non-Separable Multi-Dimensional Network Flows for Visual Computing
Viktoria Ehm Daniel Cremers Florian Bernard
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 high-dimensional 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.