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36
A Graduated Assignment Algorithm for Graph Matching
, 1996
"... A graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise. By combining graduated nonconvexity, twoway (assignment) constraints, and sparsity, large improvements in accuracy and speed are achieved. Its low order computational comp ..."
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Cited by 366 (16 self)
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A graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise. By combining graduated nonconvexity, twoway (assignment) constraints, and sparsity, large improvements in accuracy and speed are achieved. Its low order computational complexity [O(lm), where l and m are the number of links in the two graphs] and robustness in the presence of noise offer advantages over traditional combinatorial approaches. The algorithm, not restricted to any special class of graph, is applied to subgraph isomorphism, weighted graph matching, and attributed relational graph matching. To illustrate the performance of the algorithm, attributed relational graphs derived from objects are matched. Then, results from twentyfive thousand experiments conducted on 100 node random graphs of varying types (graphs with only zeroone links, weighted graphs, and graphs with node attributes and multiple link types) are reported. No comparable results have...
THIRTY YEARS OF GRAPH MATCHING IN PATTERN RECOGNITION
, 2004
"... A recent paper posed the question: "Graph Matching: What are we really talking about?". Far from providing a definite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition field. To this aim two taxonomies are presented ..."
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Cited by 214 (1 self)
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A recent paper posed the question: "Graph Matching: What are we really talking about?". Far from providing a definite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition field. To this aim two taxonomies are presented and discussed. The first includes almost all the graph matching algorithms proposed from the late seventies, and describes the different classes of algorithms. The second taxonomy considers the types of common applications of graphbased techniques in the Pattern Recognition and Machine Vision field.
Online Recognition of Chinese Characters: The StateoftheArt
 IEEE TRANS. PATTERN ANAL. MACH. INTELL
, 2004
"... Online handwriting recognition is gaining renewed interest owing to the increase of pen computing applications and new pen input devices. The recognition of Chinese characters is different from western handwriting recognition and poses a special challenge. To provide an overview of the technical s ..."
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Cited by 53 (8 self)
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Online handwriting recognition is gaining renewed interest owing to the increase of pen computing applications and new pen input devices. The recognition of Chinese characters is different from western handwriting recognition and poses a special challenge. To provide an overview of the technical status and inspire future research, this paper reviews the advances in online Chinese character recognition (OLCCR), with emphasis on the research works from the 1990s. Compared to the research in the 1980s, the research efforts in the 1990s aimed to further relax the constraints of handwriting, namely, the adherence to standard stroke orders and stroke numbers and the restriction of recognition to isolated characters only. The target of recognition has shifted from regular script to fluent script in order to better meet the requirements of practical applications. The research works are reviewed in terms of pattern representation, character classification, learning/adaptation, and contextual processing. We compare important results and discuss possible directions of future research.
Graphical models and point pattern matching
 IEEE Trans. PAMI
, 2006
"... Abstract—This paper describes a novel solution to the rigid point pattern matching problem in Euclidean spaces of any dimension. Although we assume rigid motion, jitter is allowed. We present a noniterative, polynomial time algorithm that is guaranteed to find an optimal solution for the noiseless c ..."
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Cited by 43 (6 self)
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Abstract—This paper describes a novel solution to the rigid point pattern matching problem in Euclidean spaces of any dimension. Although we assume rigid motion, jitter is allowed. We present a noniterative, polynomial time algorithm that is guaranteed to find an optimal solution for the noiseless case. First, we model point pattern matching as a weighted graph matching problem, where weights correspond to Euclidean distances between nodes. We then formulate graph matching as a problem of finding a maximum probability configuration in a graphical model. By using graph rigidity arguments, we prove that a sparse graphical model yields equivalent results to the fully connected model in the noiseless case. This allows us to obtain an algorithm that runs in polynomial time and is provably optimal for exact matching between noiseless point sets. For inexact matching, we can still apply the same algorithm to find approximately optimal solutions. Experimental results obtained by our approach show improvements in accuracy over current methods, particularly when matching patterns of different sizes. Index Terms—Point pattern matching, graph matching, graphical models, Markov random fields, junction tree algorithm. 1
StructureBased Similarity Search with Graph Histograms
 In Proceedings of the 10th International Workshop on Database & Expert Systems Applications
, 1999
"... Objects like road networks, CAD/CAM components, electrical or electronic circuits, molecules, can be represented as graphs, in many modern applications. In this paper, we propose an efficient and effective graph manipulation technique that can be used in graphbased similarity search. Given a query ..."
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Cited by 24 (0 self)
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Objects like road networks, CAD/CAM components, electrical or electronic circuits, molecules, can be represented as graphs, in many modern applications. In this paper, we propose an efficient and effective graph manipulation technique that can be used in graphbased similarity search. Given a query graph G q (V; E), we would like to determine fast which are the graphs in the database that are similar to G q (V; E), with respect to a similarity measure. First, we study the similarity measure between two graphs. Then, we discuss graph representation techniques by means of multidimensional vectors. It is shown that no false dismissals are introduced by using the vector representation. Finally we illustrate some representative queries that can be handled by our approach, and present experimental results, based on the proposed graph similarity algorithm. The results show that considerable savings are obtained with respect to computational effort and I/O operations, in comparison to conventional searching techniques.
GeneticBased Search for ErrorCorrecting Graph Isomorphism
 IEEE Transactions on Systems, Man, and Cybernetics: Part B  Cybernetics
, 1997
"... Errorcorrecting graph isomorphism has been found useful in numerous pattern recognition applications. This paper presents a geneticbased search approach that adopts genetic algorithms as the searching criteria to solve the problem of errorcorrecting graph isomorphism. By applying genetic algorith ..."
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Cited by 24 (0 self)
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Errorcorrecting graph isomorphism has been found useful in numerous pattern recognition applications. This paper presents a geneticbased search approach that adopts genetic algorithms as the searching criteria to solve the problem of errorcorrecting graph isomorphism. By applying genetic algorithms, some local search strategies are amalgamated to improve convergence speed. Besides, a selection operator is proposed to prevent premature convergence. The proposed approach has been implemented to verify its validity. Experimental results reveal the superiority of this new technique than several other wellknown algorithms. 1. INTRODUCTION Graph representation is a structural description which represents an object in terms of its parts and their interrelationships. There are several important issues in building structural description for an object such as the construction of a description from the given data, the classification of the given descriptions, etc. One of the most difficult bu...
Design and Evaluation of Spatial Similarity Approaches for Image Retrieval
 Image and Vision Computing
, 2001
"... Similarity retrieval by spatial image content (i.e., using multiple objects and their relationships in space) is an open problem which has received considerable attention in the literature. The most powerful approaches of spatial image content representation and retrieval are "Attributed Rela ..."
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Cited by 20 (4 self)
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Similarity retrieval by spatial image content (i.e., using multiple objects and their relationships in space) is an open problem which has received considerable attention in the literature. The most powerful approaches of spatial image content representation and retrieval are "Attributed Relational Graphs" (ARGs) and "Symbolic Projections" (e.g., 2D Strings). In this work, a framework is proposed for studying the performance of such spatial similarity approaches in Image DataBases (IDBs). The classical ARG and 2D string matching methods are evaluated. Several variants of ARG and 2D string methods for improving their accuracy and speedingup their time responses are also proposed and tested. A critical analysis of the performance of all these methods is presented.
Modelbased stroke extraction and matching for handwritten Chinese . . .
 Pattern Recognition
, 2001
"... This paper proposes a modelbased structuralmatching method for handwritten Chinese characterrecogercSO (HCCR). This method is able to obtain reliable stroke correspondence and enable structural interpretation. In the model base, the reference character of eachcategGv is described in an attribute ..."
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Cited by 13 (4 self)
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This paper proposes a modelbased structuralmatching method for handwritten Chinese characterrecogercSO (HCCR). This method is able to obtain reliable stroke correspondence and enable structural interpretation. In the model base, the reference character of eachcategGv is described in an attributed relationalgela (ARG). The input character is described with feature points and linesegcHJvH The strokes and interstroke relations of input character are not determined untilbeing matched with a reference character. The structuralmatching is accomplished in twostagAK candidate stroke extraction and consistentmatching All candidate input strokes to match the reference strokes are extracted by linefollowing and then the consistentmatching is achieved by heuristic search. Some structural postprocessing operations are applied to improve the stroke correspondence.Recogponde experiments were implemented on animag database collected in KAIST, andpromising results have been achieved. # 2001 PatternRecogcSK[[ Society. Published by Elsevier Science Ltd. AllrigK[ reserved.
A unifying framework for relational structure matching
 icpr
, 1998
"... The matching of relational structures is a problem that pervades computer vision and pattern recognition research. During the past few decades, two radically distinct approaches have been pursued to tackle it. The first views the matching problem as one of explicit search in statespace. The most po ..."
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Cited by 13 (4 self)
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The matching of relational structures is a problem that pervades computer vision and pattern recognition research. During the past few decades, two radically distinct approaches have been pursued to tackle it. The first views the matching problem as one of explicit search in statespace. The most popular method within this class consists of transforming it in the equivalent problem of finding a large maximal clique in a derived “association graph. ” In the second approach, the relational matching problem is viewed as one of energy minimization. In this paper, we provide a unifying framework for relational structure matching which does unify the two existing approaches. The work is centered around a remarkable result proved by Motzkin and Straus which allows us to formulate the maximum clique problem in terms of a continuous optimization problem. We present a class of continuous and discretetime “replicator ” dynamical systems developed in evolutionary game theory and show how they can naturally be employed to solve our relational matching problem. Experiments are presented which demonstrate the effectiveness of the proposed approach. 1
Graph Matching: a Fast Algorithm and its Evaluation
 In Proc. of the 14th International Conference on Pattern Recognition
, 1999
"... A graph matching algorithm is illustrated and its performance compared with that of a well known algorithm performing the same task. According to the proposed algorithm, the matching process is carried out by using a State Space Representation: a state represents a partial solution of the matching b ..."
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Cited by 12 (1 self)
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A graph matching algorithm is illustrated and its performance compared with that of a well known algorithm performing the same task. According to the proposed algorithm, the matching process is carried out by using a State Space Representation: a state represents a partial solution of the matching between two graphs, and a transition between states corresponds to the addition of a new pair of matched nodes. A set of feasibility rules is introduced for pruning states corresponding to partial matching solutions not satisfying the required graph morphism. Results outlining the computational cost reduction achieved by the method are given with reference to a set of randomly generated graphs. 1. Introduction Graphs are data structures widely used for representing information both in lowlevel and highlevel vision tasks. One of the problems of interest, with graphs, is matching a sample graph against a reference graph. Depending on the nature of the considered vision task and on the chara...