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13th iapr-TC-15 international workshop on graph-based representations in pattern recognition, gbrpr 2023

作者： Tarasiewicz, Tomasz Kawulok, Michal Department of Algorithmics and Software Silesian University of Technology Gliwice Poland

ISBN: (纸本)9783031427947

Multi-image super-resolution is a challenging computer vision problem that aims at recovering a high-resolution image from its multiple low-resolution counterparts. In recent years, deep learning-based approaches have shown promising results, however, they often lack the flexibility of modeling complex relations between pixels, permutability of the input data, or they were designed to process a specific number of input images. In this paper, we propose an improved version of our earlier graph neural network that benefits from permutation-invariant graph-based representation of multiple low-resolution images. Importantly, we demonstrate that our solution allows for performing reconstruction from a set of heterogeneous input images, which is not straightforward for other state-of-the-art techniques. Such flexibility is a crucial feature for practical applications, which is confirmed qualitatively and quantitatively for a set of real-world (rather than simulated) input images. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image fusion

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C2N-ABDP: Cluster-to-Node Attention-based Differentiable Pooling 13th

C2N-ABDP: Cluster-to-Node Attention-Based Differentiable Poo...

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13th iapr-TC-15 international workshop on graph-based representations in pattern recognition, gbrpr 2023

作者： Ye, Rongji Cui, Lixin Rossi, Luca Wang, Yue Xu, Zhuo Bai, Lu Hancock, Edwin R. Central University of Finance and Economic Beijing China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University China School of Artificial Intelligence Beijing Normal University Beijing China Department of Computer Science University of York York United Kingdom

ISBN: (纸本)9783031427947

graph neural networks have achieved state-of-the-art performance in various graph based tasks, including classification and regression at both node and graph level. In the context of graph classification, graph pooling plays an important role in reducing the number of graph nodes and allowing the graph neural network to learn a hierarchical representation of the input graph. However, most graph pooling methods fail to effectively preserve graph structure information and node feature information when reducing the number of nodes. At the same time, the existing hierarchical differentiable graph pooling methods cannot effectively calculate the importance of nodes and thus cannot effectively aggregate node information. In this paper, we propose an attention-based differentiable pooling method, which aggregates nodes into clusters when reducing the scale of the graph, uses singular value decomposition to calculate cluster information during the aggregation process, and captures node importance information through a novel attention mechanism. the experimental results show that our approach outperforms competitive models on benchmark datasets. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Aggregates

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Reducing the Computational Complexity of the Eccentricity Transform of a Tree 13th

Reducing the Computational Complexity of the Eccentricity...

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13th iapr-TC-15 international workshop on graph-based representations in pattern recognition, gbrpr 2023

作者： Banaeyan, Majid Kropatsch, Walter G. Pattern Recognition and Image Processing Group TU Wien Vienna Austria

ISBN: (纸本)9783031427947

this paper proposes a novel approach to reduce the computational complexity of the eccentricity transform (ECC) for graph-based representation and analysis of shapes. the ECC assigns to each point within a shape its geodesic distance to the furthest point, providing essential information about the shape’s geometry, connectivity, and topology. Although the ECC has proven valuable in numerous applications, its computation using traditional methods involves heavy computational complexity. To overcome this limitation, we present a method that computes the ECC of a tree, significantly reducing the computational complexity from O(n2log(n) ) to O(b), where n and b are the numbers of vertices and branching points in the tree, respectively. Our method begins by computing the ECC for tree structures, which are simpler representations of shapes. Subsequently, we introduce the concept of a 3D curve that corresponds to a smooth shape without holes, enabling the computation of the ECC for more complex shapes. By leveraging the 3D curve representation, our method provides an upper-bound approximation of the ECC, which can be effectively utilized in various applications. the proposed approach not only preserves the valuable properties of the ECC but also significantly reduces the computational burden, making it a more efficient and practical solution for graph-based representation and analysis of shapes in both 2D and 3D contexts. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computational complexity

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Cell Segmentation of in situ Transcriptomics Data Using Signed graph Partitioning 13th

Cell Segmentation of in situ Transcriptomics Data Using Sig...

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13th iapr-TC-15 international workshop on graph-based representations in pattern recognition, gbrpr 2023

作者： Andersson, Axel Behanova, Andrea Wählby, Carolina Malmberg, Filip Centre for Image Analysis Department of Information Technology and SciLifeLab BioImage Informatics Facility Uppsala University Uppsala Sweden

ISBN: (纸本)9783031427947

the locations of different mRNA molecules can be revealed by multiplexed in situ RNA detection. By assigning detected mRNA molecules to individual cells, it is possible to identify many different cell types in parallel. this in turn enables investigation of the spatial cellular architecture in tissue, which is crucial for furthering our understanding of biological processes and diseases. However, cell typing typically depends on the segmentation of cell nuclei, which is often done based on images of a DNA stain, such as DAPI. Limiting cell definition to a nuclear stain makes it fundamentally difficult to determine accurate cell borders, and thereby also difficult to assign mRNA molecules to the correct cell. As such, we have developed a computational tool that segments cells solely based on the local composition of mRNA molecules. First, a small neural network is trained to compute attractive and repulsive edges between pairs of mRNA molecules. the signed graph is then partitioned by a mutex watershed into components corresponding to different cells. We evaluated our method on two publicly available datasets and compared it against the current state-of-the-art and older baselines. We conclude that combining neural networks with combinatorial optimization is a promising approach for cell segmentation of in situ transcriptomics data. the tool is open-source and publicly available for use at https://***/wahlby-lab/IS3G. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cells

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graph polynomials, principal pivoting, and maximum independent sets

Graph polynomials, principal pivoting, and maximum independe...

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4th international workshop on graph based representations in pattern recognition

作者： Glantz, R Pelillo, M Univ Ca Foscari Venezia Dipartimento Informat I-30172 Venice Italy

ISBN: (纸本)354040452X

the maximum independent set problem (or its equivalent formulation, which asks for maximum cliques) is a well-known difficult combinatorial optimization problem that is frequently encountered in computer vision and pattern recognition. Recently, motivated by a linear complementarity formulation, standard pivoting operations on matrices have proven to be effective in attacking this and related problems. An intriguing connection between the maximum independent set problem and pivoting has also been recently studied by Arratia, Bollobas and Sorkin who introduced the interlace polynomial, a graph polynomial defined in terms of a new pivoting operation on undirected, unweighted graphs. Specifically, they proved that the degree of this polynomial is an upper bound on the independence number of a graph. the first contribution of this paper is to interpret their work in terms of standard matrix pivoting. We show that Arratia et al.'s pivoting operation on a graph is equivalent to a principal pivoting transform on a corresponding adjacency matrix, provided that all calculations are performed in the Galois field F-2. We then extend Arratia et al.'s pivoting operation to fields other than F-2, thereby allowing us to apply their polynomial to the class of gain graphs, namely bidirected edge-weighted graphs whereby reversed edges carry weights that differ only by their sign. Finally, we introduce a new graph polynomial for undirected graphs. Its recursive calculation can be done such that all ends of the recursion correspond to independent sets and its degree equals the independence number. However, the new graph polynomial is different from the independence polynomial.

关键词： Polynomials

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GNN-DES: A New End-to-End Dynamic Ensemble Selection Method based on Multi-label graph Neural Network 13th

GNN-DES: A New End-to-End Dynamic Ensemble Selection Method ...

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13th iapr-TC-15 international workshop on graph-based representations in pattern recognition, gbrpr 2023

作者： de Araujo Souza, Mariana Sabourin, Robert da Cunha Cavalcanti, George Darmiton e Cruz, Rafael Menelau Oliveira École de Technologie Supérieure Université du Québec MontréalQC Canada Centro de Informática Universidade Federal de Pernambuco Pernambuco Recife Brazil

ISBN: (纸本)9783031427947

Most dynamic ensemble selection (DES) techniques rely solely on local information to single out the most competent classifiers. However, data sparsity and class overlap may hinder the region definition step, yielding an unreliable local context for performing the selection task. thus, we propose in this work a DES technique that uses both the local information and classifiers’ interactions to learn the ensemble combination rule. To that end, we encode the local information into a graph structure and the classifiers’ information into multiple meta-labels, and learn the DES technique end-to-end using a multi-label graph neural network (GNN). Experimental results over 35 high-dimensional problems show the proposed method outperforms most evaluated DES techniques as well as the static baseline, suggesting its suitability for dealing with sparse overlapped data. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： graph neural networks

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graph Normalizing Flows to Pre-image Free Machine Learning for Regression 13th

Graph Normalizing Flows to Pre-image Free Machine Learning ...

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13th iapr-TC-15 international workshop on graph-based representations in pattern recognition, gbrpr 2023

作者： Glédel, Clément Gaüzère, Benoît Honeine, Paul Univ Rouen Normandie INSA Rouen Normandie Université Le Havre Normandie Normandie Univ LITIS UR 4108 Rouen76000 France

ISBN: (纸本)9783031427947

In Machine Learning, data embedding is a fundamental aspect of creating nonlinear models. However, they often lack interpretability due to the limited access to the embedding space, also called latent space. As a result, it is highly desirable to represent, in the input space, elements from the embedding space. Nevertheless, obtaining the inverse embedding is a challenging task, and it involves solving the hard pre-image problem. this task becomes even more challenging when dealing with structured data like graphs, which are complex and discrete by nature. this article presents a novel approach for graph regression using Normalizing Flows (NFs), in order to avoid the pre-image problem. By creating a latent representation space using a NF, the method overcomes the difficulty of finding an inverse transformation. the approach aims at supervising the space generation process in order to create a space suitable for the specific regression task. Furthermore, any result obtained in the generated space can be translated into the input space through the application of the inverse transformation learned by the model. the effectiveness of our approach is demonstrated by using a NF model on different regression problems. We validate the ability of the method to efficiently handle both the pre-image generation and the regression task. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Using kernels on hierarchical graphs in automatic classification of designs

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2011年 6658 LNCS卷 335-344页

作者： Strug, Barbara Department of Physics Astronomy and Applied Computer Science Jagiellonian University Reymonta 4 Krakow Poland

ISBN: (纸本)9783642208430

In this paper the use of kernel methods in automatic classification of hierarchical graphs is presented. the classification is used as a basis for evaluation of designs in a computer aided design system. A kernel for hierarchical graphs based on a combination of tree and graph kernels is proposed. Hierarchical graphs-based representation used in design problems is briefly presented. the proposed approach is tested in experiment on a flat layout design task and preliminary result are also presented. © 2011 Springer-Verlag Berlin Heidelberg.

关键词： Trees (mathematics)

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2011年 6658 LNCS卷 V-VI页

作者： Jiang, Xiaoyi Ferrer, Miquel Torsello, Andrea University of Münster Department of Mathematics and Computer Science Einsteinstraße 62 48149 Münster Germany Universitat Politècnica de Catalunya Institut de Robòtica i Informatica Industrial C. Llorens i Artigas 4-6 08028 Barcelona Spain Università Ca' Foscari di Venezia Dipartimento di Scienze Ambientali Informatica Statistica Via Torino 155 30172 Venezia Mestre Italy

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