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9th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2013

作者： De Sousa, Samuel Artner, Nicole M. Kropatsch, Walter G. Vienna University of Technology Pattern Recognition and Image Processing Group Vienna Austria

ISBN: (纸本)9783642382208

graph centrality has been extensively applied in Social Network Analysis to model the interaction of actors and the information flow inside a graph. In this paper, we investigate the usage of graph centralities in the Shape Matching task. We create a graph-based representation of a shape and describe this graph by using different centrality measures. We build a Naive Bayes classifier whose input feature vector consists of the measurements obtained by the centralities and evaluate the different performances for each centrality. © 2013 Springer-Verlag.

关键词： graphic methods

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graph-based representations in pattern recognition 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Mario Vento Pasquale Foggia Vincenzo Carletti Donatello Conte

ISBN: (数字)9783031427954

ISBN: (纸本)9783031427947

this book constitutes the refereed proceedings of the 13th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2023, which took place in Vietri sul Mare, Italy, in September 2023.;the 16 full papers included in this book were carefully reviewed and selected from 18 submissions. they were organized in topical sections on graph kernels and graph algorithms; graph neural networks; and graph-based representations and applications.

关键词： pattern recognition Discrete Mathematics in Computer Science Computer graphics Algorithms Data Structures and Information theory Artificial Intelligence

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Directly computing the generators of image homology using graph pyramids

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IMAGE AND VISION COMPUTING 2009年第7期27卷 846-853页

作者： Peltier, Samuel Ion, Adrian Kropatsch, Walter G. Damiand, Guillaume Haxhimusa, Yll Vienna Univ Technol Pattern Recognit & Image Proc Grp Fac Informat A-1040 Vienna Austria Univ Lyon CNRS LIRIS UMR5205 F-69622 Villeurbanne France Purdue Univ Dept Psychol Sci W Lafayette IN 47907 USA

We introduce a method for computing homology groups and their generators of a 2D image, using a hierarchical structure, i.e. irregular graph pyramid. Starting from an image, a hierarchy of the image is built by two operations that preserve homology of each region. Instead of computing homology generators in the base where the number of entities (cells) is large, we first reduce the number of cells by a graph pyramid. then homology generators are computed efficiently on the top level of the pyramid, since the number of cells is small. A top down process is then used to deduce homology generators in any level of the pyramid, including the base level, i.e. the initial image. the produced generators fit on the object boundaries. A unique set of generators called the minimal set, is defined and its computation is discussed. We show that the new method produces valid homology generators and present some experimental results. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Homology generators Irregular graph pyramids

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Mobile Assisted Living: Smartwatch-based Fall Risk Assessment for Elderly People 18

Mobile Assisted Living: Smartwatch-based Fall Risk Assessmen...

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5th international workshop on Sensor-based Activity recognition and Interaction (iWOAR)

作者： Haescher, Marian Matthies, Denys J. C. Srinivasan, Karthik Bieber, Gerald Fraunhofer Inst Comp Graph Res IGD Rostock Rostock Germany Univ Auckland Auckland Bioengn Inst Augmented Human Lab Auckland New Zealand Next Step Dynam AB Malmo Sweden

ISBN: (纸本)9781450364874

We present a novel Smartwatch-based approach, to enable Mobile Assisted Living (MAL) for users with special needs. A major focus group for this approach are elderly people. We developed a tool for caregivers applicable in home environments, nursing care, and hospitals, to assess the vitality of their patients. Hereby, we particularly focus on the prediction of falls, because falls are a major reason for serious injuries and premature death among elderly. therefore, we propose a multi parametric score based on standardized fall risk assessment tests, as well as on sleep quality, medication, patient history, motor skills, and environmental factors. the resulting total fall risk score reflects individual changes in behavior and vitality, which consequently enables for fall preventing interventions. Our system has been deployed and evaluated in a pilot study among 30 elderly patients over a period of four weeks.

关键词： Mobile Assisted Living Fall Risk Assistance Elderly People Smartwatch Health Care Health Technology Pulse Detection Vital Signs pattern recognition

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Efficient Dense-graph Convolutional Network with Inductive Prior Augmentations for Unsupervised Micro-Gesture recognition 26

Efficient Dense-Graph Convolutional Network with Inductive P...

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26th international Conference on pattern recognition / 8th international workshop on Image Mining - theory and Applications (IMTA)

作者： Shah, Atif Chen, Haoyu Shi, Henglin Zhao, Guoying Univ Oulu Ctr Machine Vis & Signal Anal CMVS Oulu Finland

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Skeleton-based action/gesture recognition has already witnessed excellent progress on processing large-scale, laboratory-based datasets with pre-defined skeleton joint topology. However, it's still an unsolved task when it comes to real-world scenarios with practical limitations such as small-scaled dataset sizes, few-labeled samples, and various skeleton topologies. In this paper, we work on the recognition of micro-gestures, which are subtle body gestures collected in real-world scenarios. Specifically, we utilize contrastive learning to heritage the knowledge from known large-scale datasets for enhancing the learning on fewer samples of micro-gestures. To overcome the gap caused by various domain distributions and structure topologies between the datasets, we compute skeleton representations from augmented sequences via momentum-based efficient and scalable encoders as additional inductive priors. Importantly, we propose an effective dense-graph based unsupervised architecture that resorts to a queue-based dictionary to store positive and negative keys for better contrast with queries to learn substantially efficient and discriminant patterns in the feature space. Together with cross-dataset experimental results show that our model significantly improves the accuracies on two micro-gesture datasets, SMG by 7.4% and iMiGUE by 18.41% advocating its superiority.

关键词： Dictionaries Computer architecture Skeleton Robustness Data models Topology pattern recognition

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STIT: Spatio-Temporal Interaction Transformers for Human-Object Interaction recognition in Videos 26

STIT: Spatio-Temporal Interaction Transformers for Human-Obj...

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26th international Conference on pattern recognition / 8th international workshop on Image Mining - theory and Applications (IMTA)

作者： Almushyti, Muna Li, Frederick W. B. Univ Durham Dept Comp Sci Durham England Qassim Univ Educ Serv Buraydah Saudi Arabia

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Recognizing human-object interactions is challenging due to their spatio-temporal changes. We propose the Spatio-Temporal Interaction Transformer-based (STIT) network to reason such changes. Specifically, spatial transformers learn humans and objects context at specific frame time. Temporal transformer then learns the relations at a higher level between spatial context representations at different time steps, capturing long-term dependencies across frames. We further investigate multiple hierarchy designs in learning human interactions. We achieved superior performance on Charades, Something-Something v1 and CAD-120 datasets, comparing to baseline models without learning human-object relations, or with prior graph-based networks. We also achieved state-of-the-art accuracy of 95.93% on CAD-120 dataset [1] by employing RGB data only.

关键词： Deep learning Transformers pattern recognition Videos Context modeling

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Speeding Up graph Edit Distance Computation through Fast Bipartite Matching

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8th IAPR-TC-15 international workshop on graph-based representations in pattern recognition (GbRPR)

作者： Fankhauser, Stefan Riesen, Kaspar Bunke, Horst Univ Bern Inst Comp Sci & Appl Math CH-3012 Bern Switzerland

ISBN: (纸本)9783642208447

In the field of structural pattern recognition graphs constitute a very common and powerful way of representing objects. the main drawback of graph representations is that the computation of various graph similarity measures is exponential in the number of involved nodes. Hence, such computations are feasible for rather small graphs only. One of the most flexible graph similarity measures is graph edit distance. In this paper we propose a novel approach for the efficient computation of graph edit distance based on bipartite graph matching by means of the Volgenant-Jonker assignment algorithm. Our proposed algorithm provides only suboptimal edit distances, but runs in polynomial time. the reason for its sub-optimality is that edge information is taken into account only in a limited fashion during the process of finding the optimal node assignment between two graphs. In experiments on diverse graph representations we demonstrate a high speed up of our proposed method over a traditional algorithm for graph edit distance computation and over two other sub-optimal approaches that use the Hungarian and Munkres algorithm. Also, we show that classification accuracy remains nearly unaffected by the suboptimal nature of the algorithm.

关键词： pattern matching

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Comparing string representations and distances in a natural images classification task

Comparing string representations and distances in a natural ...

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5th IAPR international workshop on graph-based representations in pattern recognition, GbRPR 2005

作者： Ros, Julien Laurent, Christophe Jolion, Jean-Michel Simand, Isabelle France Telecom R and D TECH IRIS 4 rue du Clos Courtel 35512 Cesson Sevigne Cedex France LIRIS PRE CNRS 3672 INSA INSA Lyon 9621 Villeurbanne cedex France

this paper shows how strings can be used in a natural images classification task. We propose to build an attributed string from a set of regions of interest detected thanks to an interest point detector. these salient zones are characterized by local signatures describing singularities and they are linked by using graph seriation algorithms and perceptual methods. Once each image is represented by a string of signatures, we propose to use string-based edit distances and an ordered histograms-based distance in order to perform the classification task. Experiments have shown that whereas seriation algorithms give approximately the same results, the ordered histogram based distance is more efficient for the considered application. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Image analysis

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Cypher-based graph pattern matching in gradoop 5

Cypher-based graph pattern matching in gradoop

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5th international workshop on graph Data Management Experiences and Systems, GRADES 2017

作者： Junghanns, Martin Kießling, Max Averbuch, Alex Petermann, Andre Rahm, Erhard University of Leipzig and ScaDS Dresden/Leipzig Germany

ISBN: (纸本)9781450350389

graph pattern matching is an important and challenging operation on graph data. Typical use cases are related to graph analytics. Since analysts are otten non-programmers, a graph system will only gain acceptance, if there is a comprehensible way to declare pattern matching queries. However, respective query languages are currently only supported by graph databases but not by distributed graph processing systems. To enable pattern matching on a large scale, we implemented the declarative graph query language Cypher within the distributed graph analysis platform Gradoop. Using LDBC graph data, we show that our query engine is scalable for operational as well as analytical workloads. tte implementation is open-source and easy to extend for further research. © 2017 ACM.

关键词： pattern matching

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Minimizing the topological structure of line images 7th

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7th Joint IAPR international workshop on Structural and Syntactic pattern recognition, SSPR 1998 and 2nd international workshop on Statistical Techniques in pattern recognition, SPR 1998

作者： Kropatsch, Walter G. Burge, Mark Vienna University of Technology Institute of Automation 183/2 Pattern Recognition and Image Processing Group Treitlstr.3 WienA-1040 Austria Johannes Kepler University Institute of Systems Science Computer Vision Laboratory LinzA-4040 Austria

ISBN: (纸本)3540648585

We present a new algorithm based on Dual graph Contraction (DGC) to transform the Run graph into its Minimum Line Property Preserving (MLPP) form which, when implemented in parallel, requires O(log(longestcurve)) steps. A MLPP graph of a line image compliments the structural information in geometric graph representations like the run graph. Using such a graph and its dual, line image analysis systems can efficiently detect topological features like loops and holes and make use of relations like containment. © Springer-Verlag Berlin Heidelberg 1998.

关键词： Topology

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