Error-Correcting Isomorphisms of Attributed Relational Graphs for Pattern Analysis

Tsai, Wen‐Hsiang; Fu, King-Sun

doi:10.1109/tsmc.1979.4310127

Cited by 268 publications

(121 citation statements)

References 15 publications

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“…Clusters sorted according to their matching cost (ranking) Clusters with their matching cost Finally, an inexact graph matching method based on a customized version of the edit distance algorithm [12,13] is employed to find clusters with the highest structural and content similarity to the model graph. Similar algorithms have been used for various purposes, such as computer vision [16], pattern recognition [14], and retrieving relevant principles from ethics cases [15].…”

Section: Resultsmentioning

confidence: 99%

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What’s in a Cluster? Automatically Detecting Interesting Interactions in Student E-Discussions

Miksatko

McLaren

2008

Intelligent Tutoring Systems

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Abstract. Students in classrooms are starting to use visual argumentation tools for e-discussions -a form of debate in which contributions are written into graphical shapes and linked to one another according to whether they, for instance, support or oppose one another. In order to moderate several simultaneous e-discussions effectively, teachers must be alerted regarding events of interest. We focused on the identification of clusters of contributions representing interaction patterns that are of pedagogical interest (e.g., a student clarifies his or her opinion and then gets feedback from other students). We designed an algorithm that takes an example cluster as input and uses inexact graph matching, text analysis, and machine learning classifiers to search for similar patterns in a given corpus. The method was evaluated on an annotated dataset of real ediscussions and was able to detect almost 80% of the annotated clusters while providing acceptable precision performance.

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Section: Resultsmentioning

confidence: 99%

“…Note that the detection of all subgraphs is an NP-Complete problem but only in theoretical, not practical, terms. The search space is significantly reduced by applying heuristics similar to [13,16] and the method performs well on graphs of moderate size (dozens of vertices). The graphs in our particular domain are certainly within this range.…”

Section: Resultsmentioning

confidence: 99%

What’s in a Cluster? Automatically Detecting Interesting Interactions in Student E-Discussions

Miksatko

McLaren

2008

Intelligent Tutoring Systems

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“…The idea behind edit distance (Tsai and Fu, 1979) is that it is possible to identify a set of basic edit operations on nodes and edges of a structure, and to associate with these operations a cost. The edit distance is found by searching for the sequence of edit operations that will make the two graphs isomorphic with one another and which has minimum cost.…”

Section: Inexact Tree Matching As a Common Substructure Problemmentioning

confidence: 99%

Computing approximate tree edit distance using relaxation labeling

Torsello

Hancock

2003

Pattern Recognition Letters

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This paper presents a new method for computing the tree edit distance problem with uniform edit cost. We commence by showing that any tree obtained with a sequence of cut operations is a subtree of the transitive closure of the original tree, we show that the necessary condition for any subtree to be a solution can be reduced to a clique problem in a derived structure. Using this idea we transform the problem of computing tree edit distance into a series of maximum weight clique problems. We, then use relaxation labeling to find an approximation to the tree edit distance.

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“…Attributed Graphs (AGs) are the basic tool for structural pattern recognition and have been widely used in the literature since they were proposed by Tsai and Fu in the latest seventies [17]. Whereas a graph consists of a set of nodes (or vertices) representing pattern primitives and a set of edges (or arcs) representing relations between the primitives, AGs incorporate semantic information about the properties of both the primitives and the relations, through the use of vertex and arc attributes, respectively.…”

Section: Representation and Recognition Of Objects Using Function-desmentioning

confidence: 99%

Graph-based representations and techniques for image processing and image analysis

Sanfeliu

Alquézar

Andrade

et al. 2002

Pattern Recognition

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In this paper we will discuss the use of some graph-based representations and techniques for image processing and analysis. Instead of making an extensive review of the graph techniques in this field, we will explain how we are using these techniques in an active vision system for an autonomous mobile robot developed in the Institut de Robòtica i Informàtica Industrial within the project "Active Vision System with Automatic Learning Capacity for Industrial Applications (CICYT TAP98-0473)". Specifically we will discuss the use of graph-based representations and techniques for image segmentation, image perceptual grouping and object recognition. We first present a generalisation of a graph partitioning greedy algorithm for colour image segmentation. Next we describe a novel fusion of colour-based segmentation and depth from stereo that yields a graph representing every object in the scene. Finally we describe a new representation of a set of attributed graphs (AGs), denominated Function Described Graphs (FDGs), a distance measure for matching AGs with FDGs and some applications for robot vision.

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Error-Correcting Isomorphisms of Attributed Relational Graphs for Pattern Analysis

Cited by 268 publications

References 15 publications

What’s in a Cluster? Automatically Detecting Interesting Interactions in Student E-Discussions

What’s in a Cluster? Automatically Detecting Interesting Interactions in Student E-Discussions

Computing approximate tree edit distance using relaxation labeling

Graph-based representations and techniques for image processing and image analysis

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