103 research outputs found
Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts
Formulations of the Image Decomposition Problem as a Multicut Problem (MP)
w.r.t. a superpixel graph have received considerable attention. In contrast,
instances of the MP w.r.t. a pixel grid graph have received little attention,
firstly, because the MP is NP-hard and instances w.r.t. a pixel grid graph are
hard to solve in practice, and, secondly, due to the lack of long-range terms
in the objective function of the MP. We propose a generalization of the MP with
long-range terms (LMP). We design and implement two efficient algorithms
(primal feasible heuristics) for the MP and LMP which allow us to study
instances of both problems w.r.t. the pixel grid graphs of the images in the
BSDS-500 benchmark. The decompositions we obtain do not differ significantly
from the state of the art, suggesting that the LMP is a competitive formulation
of the Image Decomposition Problem. To demonstrate the generality of the LMP,
we apply it also to the Mesh Decomposition Problem posed by the Princeton
benchmark, obtaining state-of-the-art decompositions
Schnyder decompositions for regular plane graphs and application to drawing
Schnyder woods are decompositions of simple triangulations into three
edge-disjoint spanning trees crossing each other in a specific way. In this
article, we define a generalization of Schnyder woods to -angulations (plane
graphs with faces of degree ) for all . A \emph{Schnyder
decomposition} is a set of spanning forests crossing each other in a
specific way, and such that each internal edge is part of exactly of the
spanning forests. We show that a Schnyder decomposition exists if and only if
the girth of the -angulation is . As in the case of Schnyder woods
(), there are alternative formulations in terms of orientations
("fractional" orientations when ) and in terms of corner-labellings.
Moreover, the set of Schnyder decompositions on a fixed -angulation of girth
is a distributive lattice. We also show that the structures dual to
Schnyder decompositions (on -regular plane graphs of mincut rooted at a
vertex ) are decompositions into spanning trees rooted at such
that each edge not incident to is used in opposite directions by two
trees. Additionally, for even values of , we show that a subclass of
Schnyder decompositions, which are called even, enjoy additional properties
that yield a reduced formulation; in the case d=4, these correspond to
well-studied structures on simple quadrangulations (2-orientations and
partitions into 2 spanning trees). In the case d=4, the dual of even Schnyder
decompositions yields (planar) orthogonal and straight-line drawing algorithms.
For a 4-regular plane graph of mincut 4 with vertices plus a marked
vertex , the vertices of are placed on a grid according to a permutation pattern, and in the orthogonal drawing
each of the edges of has exactly one bend. Embedding
also the marked vertex is doable at the cost of two additional rows and
columns and 8 additional bends for the 4 edges incident to . We propose a
further compaction step for the drawing algorithm and show that the obtained
grid-size is strongly concentrated around for a uniformly
random instance with vertices
A Schnyder-type drawing algorithm for 5-connected triangulations
We define some Schnyder-type combinatorial structures on a class of planar
triangulations of the pentagon which are closely related to 5-connected
triangulations. The combinatorial structures have three incarnations defined in
terms of orientations, corner-labelings, and woods respectively. The wood
incarnation consists in 5 spanning trees crossing each other in an orderly
fashion. Similarly as for Schnyder woods on triangulations, it induces, for
each vertex, a partition of the inner triangles into face-connected regions
(5~regions here). We show that the induced barycentric vertex-placement, where
each vertex is at the barycenter of the 5 outer vertices with weights given by
the number of faces in each region, yields a planar straight-line drawing.Comment: Appears in the Proceedings of the 31st International Symposium on
Graph Drawing and Network Visualization (GD 2023
Generalizations of the Multicut Problem for Computer Vision
Graph decomposition has always been a very important concept in machine learning and computer vision. Many tasks like image and mesh segmentation, community detection in social networks, as well as object tracking and human pose estimation can be formulated as a graph decomposition problem. The multicut problem in particular is a popular model to optimize for a decomposition of a given graph. Its main advantage is that no prior knowledge about the number of components or their sizes is required. However, it has several limitations, which we address in this thesis: Firstly, the multicut problem allows to specify only cost or reward for putting two direct neighbours into distinct components. This limits the expressibility of the cost function. We introduce special edges into the graph that allow to define cost or reward for putting any two vertices into distinct components, while preserving the original set of feasible solutions. We show that this considerably improves the quality of image and mesh segmentations. Second, multicut is notorious to be NP-hard for general graphs, that limits its applications to small super-pixel graphs. We define and implement two primal feasible heuristics to solve the problem. They do not provide any guarantees on the runtime or quality of solutions, but in practice show good convergence behaviour. We perform an extensive comparison on multiple graphs of different sizes and properties. Third, we extend the multicut framework by introducing node labels, so that we can jointly optimize for graph decomposition and nodes classification by means of exactly the same optimization algorithm, thus eliminating the need to hand-tune optimizers for a particular task. To prove its universality we applied it to diverse computer vision tasks, including human pose estimation, multiple object tracking, and instance-aware semantic segmentation. We show that we can improve the results over the prior art using exactly the same data as in the original works. Finally, we use employ multicuts in two applications: 1) a client-server tool for interactive video segmentation: After the pre-processing of the video a user draws strokes on several frames and a time-coherent segmentation of the entire video is performed on-the-fly. 2) we formulate a method for simultaneous segmentation and tracking of living cells in microscopy data. This task is challenging as cells split and our algorithm accounts for this, creating parental hierarchies. We also present results on multiple model fitting. We find models in data heavily corrupted by noise by finding components defining these models using higher order multicuts. We introduce an interesting extension that allows our optimization to pick better hyperparameters for each discovered model. In summary, this thesis extends the multicut problem in different directions, proposes algorithms for optimization, and applies it to novel data and settings.Die Zerlegung von Graphen ist ein sehr wichtiges Konzept im maschinellen Lernen und maschinellen Sehen. Viele Aufgaben wie Bild- und Gittersegmentierung, Kommunitätserkennung in sozialen Netzwerken, sowie Objektverfolgung und Schätzung von menschlichen Posen können als Graphzerlegungsproblem formuliert werden. Der Mehrfachschnitt-Ansatz ist ein populäres Mittel um über die Zerlegungen eines gegebenen Graphen zu optimieren. Sein größter Vorteil ist, dass kein Vorwissen über die Anzahl an Komponenten und deren Größen benötigt wird. Dennoch hat er mehrere ernsthafte Limitierungen, welche wir in dieser Arbeit behandeln: Erstens erlaubt der klassische Mehrfachschnitt nur die Spezifikation von Kosten oder Belohnungen für die Trennung von zwei Nachbarn in verschiedene Komponenten. Dies schränkt die Ausdrucksfähigkeit der Kostenfunktion ein und führt zu suboptimalen Ergebnissen. Wir fügen dem Graphen spezielle Kanten hinzu, welche es erlauben, Kosten oder Belohnungen für die Trennung von beliebigen Paaren von Knoten in verschiedene Komponenten zu definieren, ohne die Menge an zulässigen Lösungen zu verändern. Wir zeigen, dass dies die Qualität von Bild- und Gittersegmentierungen deutlich verbessert. Zweitens ist das Mehrfachschnittproblem berüchtigt dafür NP-schwer für allgemeine Graphen zu sein, was die Anwendungen auf kleine superpixel-basierte Graphen einschränkt. Wir definieren und implementieren zwei primal-zulässige Heuristiken um das Problem zu lösen. Diese geben keine Garantien bezüglich der Laufzeit oder der Qualität der Lösungen, zeigen in der Praxis jedoch gutes Konvergenzverhalten. Wir führen einen ausführlichen Vergleich auf vielen Graphen verschiedener Größen und Eigenschaften durch. Drittens erweitern wir den Mehrfachschnitt-Ansatz um Knoten-Kennzeichnungen, sodass wir gemeinsam über Zerlegungen und Knoten-Klassifikationen mit dem gleichen Optimierungs-Algorithmus optimieren können. Dadurch wird der Bedarf der Feinabstimmung einzelner aufgabenspezifischer Löser aus dem Weg geräumt. Um die Allgemeingültigkeit dieses Ansatzes zu überprüfen, haben wir ihn auf verschiedenen Aufgaben des maschinellen Sehens, einschließlich menschliche Posenschätzung, Mehrobjektverfolgung und instanz-bewusste semantische Segmentierung, angewandt. Wir zeigen, dass wir Resultate von vorherigen Arbeiten mit exakt den gleichen Daten verbessern können. Abschließend benutzen wir Mehrfachschnitte in zwei Anwendungen: 1) Ein Nutzer-Server-Werkzeug für interaktive Video Segmentierung: Nach der Vorbearbeitung eines Videos zeichnet der Nutzer Striche auf mehrere Einzelbilder und eine zeit-kohärente Segmentierung des gesamten Videos wird in Echtzeit berechnet. 2) Wir formulieren eine Methode für simultane Segmentierung und Verfolgung von lebenden Zellen in Mikroskopie-Aufnahmen. Diese Aufgabe ist anspruchsvoll, da Zellen sich aufteilen und unser Algorithmus dies in der Erstellung von Eltern-Hierarchien mitberücksichtigen muss. Wir präsentieren außerdem Resultate zur Mehrmodellanpassung. Wir berechnen Modelle in stark verrauschten Daten indem wir mithilfe von Mehrfachschnitten höherer Ordnung Komponenten finden, die diesen Modellen entsprechen. Wir führen eine interessante Erweiterung ein, die es unserer Optimierung erlaubt, bessere Hyperparameter für jedes entdeckte Modell auszuwählen. Zusammenfassend erweitert diese Arbeit den Mehrfachschnitt-Ansatz in unterschiedlichen Richtungen, schlägt Algorithmen zur Inferenz in den resultierenden Modellen vor und wendet ihn auf neuartigen Daten und Umgebungen an
Combinatorics
Combinatorics is a fundamental mathematical discipline that focuses on the study of discrete objects and their
properties. The present workshop featured research in such diverse areas as Extremal, Probabilistic
and Algebraic Combinatorics, Graph Theory, Discrete Geometry, Combinatorial Optimization,
Theory of Computation and Statistical Mechanics. It provided current accounts of exciting developments and challenges in these fields and a stimulating venue for a variety of fruitful interactions.
This is a report on the meeting, containing extended abstracts of the presentations and a summary of the problem session
- …