A graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise. By combining graduated non-convexity, 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 twenty-five thousand experiments conducted on 100 node random graphs of varying types (graphs with only zero-one links, weighted graphs, and graphs with node attributes and multiple link types) are reported. No comparable results have...

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.

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 graph-based 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.

Error-correcting graph isomorphism has been found useful in numerous pattern recognition applications. This paper presents a genetic-based search approach that adopts genetic algorithms as the searching criteria to solve the problem of error-correcting 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 well-known 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...

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 speeding-up their time responses are also proposed and tested. A critical analysis of the performance of all these methods is presented.

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

. Attributed relational graphs have shown good adequacy for representation and analysis in computer vision. In some applications, pattern recognition requires that an attributed graph representation of an object is matched with several similar graphs stored in a database. However, there is no single algorithm that could be recommended for this problem. In this study, some well-known attributed graph matching algorithms were compared to find out the relevant properties and the types of problems they are best suited for. In addition, an algorithm based on hash coding, developed by the authors, was considered. Some test results are given on both the speed and classification accuracy of the algorithms for artificial and real images. 1. INTRODUCTION Attributed graphs have turned out to be very useful data structures for image representation and understanding in Computer Vision systems [ 10 ] . Examples of graph-based representations of patterns include the picture languages of Shaw [ 11 ] ,...

This paper proposes a model-based 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 inter-stroke 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 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 low-level and high-level 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...

Subgraph isomorphism detection is a fundamental technique in computer vision. In this paper we propose a new subgraph matching procedure that is particularly useful if the number of prototype graphs is large, and if the graph representation of the image to be interpreted is dynamically changing. Our procedure is derived from the RETE-matching algorithm that has been developed for forward chaining rule-based systems [1]. We introduce our new method and discuss its computational complexity. It will be shown that the computational complexity of the proposed approach is not better than that of a naive, straigth-forward solution to the problem. In the best case, however, a significant speedup can be achieved. Finally, we show experimental results which confirm our theoretical complexity analysis. 1 Introduction Graph matching is a fundamental technique in computer vision and image understanding. In many vision systems a graph extracted from an image is matched to stored model gra...