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43
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 354 (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...
Structural Matching in Computer Vision Using Probabilistic Reasoning
, 1995
"... easurement error distributions is dependent on the type of geometric feature, the measurement noise model and the nature of the unknown scenetomodel transformation: some examples are presented. A number of variations on the basic labelling algorithm are described, of which some have implications f ..."
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Cited by 199 (15 self)
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easurement error distributions is dependent on the type of geometric feature, the measurement noise model and the nature of the unknown scenetomodel transformation: some examples are presented. A number of variations on the basic labelling algorithm are described, of which some have implications for realtime applications. The algorithm can also be readily implementated on several different types of parallelprocessing computers. Key words: Matching, Labelling, Probabilistic Relaxation, Object Recognition. Email: w.christmas@ee.surrey.ac.uk WWW: http://www.surrey.ac.uk/ Acknowledgements I would like to thank my supervisors, Josef Kittler and Maria Petrou, for their guidance and stimulating discussions during the course of this work, and for providing the ideas and motivation that led to the work in the first place. I would also like to thank my other colleagues in the VSSP Group, for their interest and discussions. In particular thanks are due to Ge
Learning Graph Matching
"... As a fundamental problem in pattern recognition, graph matching has found a variety of applications in the field of computer vision. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs. There are many way ..."
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Cited by 77 (9 self)
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As a fundamental problem in pattern recognition, graph matching has found a variety of applications in the field of computer vision. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs. There are many ways in which the problem has been formulated, but most can be cast in general as a quadratic assignment problem, where a linear term in the objective function encodes node compatibility functions and a quadratic term encodes edge compatibility functions. The main research focus in this theme is about designing efficient algorithms for solving approximately the quadratic assignment problem, since it is NPhard. In this paper, we turn our attention to the complementary problem: how to estimate compatibility functions such that the solution of the resulting graph matching problem best matches the expected solution that a human would manually provide. We present a method for learning graph matching: the training examples are pairs of graphs and the “labels” are matchings between pairs of graphs. We present experimental results with real image data which give evidence that learning can improve the performance of standard graph matching algorithms. In particular, it turns out that linear assignment with such a learning scheme may improve over stateoftheart quadratic assignment relaxations. This finding suggests that for a range of problems where quadratic assignment was thought to be essential for securing good results, linear assignment, which is far more efficient, could be just sufficient if learning is performed. This enables speedups of graph matching by up to 4 orders of magnitude while retaining stateoftheart accuracy. 1.
Supervised Learning of Large Perceptual Organization: Graph Spectral Partitioning and Learning Automata
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2000
"... this article, please send email to: tpami@computer.org, and reference IEEECS Log Number 107780 ..."
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Cited by 69 (6 self)
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this article, please send email to: tpami@computer.org, and reference IEEECS Log Number 107780
The Dynamics of Nonlinear Relaxation Labeling Processes
, 1997
"... We present some new results which definitively explain the behavior of the classical, heuristic nonlinear relaxation labeling algorithm of Rosenfeld, Hummel, and Zucker in terms of the HummelZucker consistency theory and dynamical systems theory. In particular, it is shown that, when a certain symm ..."
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Cited by 37 (11 self)
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We present some new results which definitively explain the behavior of the classical, heuristic nonlinear relaxation labeling algorithm of Rosenfeld, Hummel, and Zucker in terms of the HummelZucker consistency theory and dynamical systems theory. In particular, it is shown that, when a certain symmetry condition is met, the algorithm possesses a Liapunov function which turns out to be (the negative of) a wellknown consistency measure. This follows almost immediately from a powerful result of Baum and Eagon developed in the context of Markov chain theory. Moreover, it is seen that most of the essential dynamical properties of the algorithm are retained when the symmetry restriction is relaxed. These properties are also shown to naturally generalize to higherorder relaxation schemes. Some applications and implications of the presented results are finally outlined.
An Environment for Morphosyntactic Processing of Unrestricted Spanish Text
, 1998
"... We present in this paper a fast, broadcoverage, accurate morphological analyzer for Spanish words, MACO+, which is an extended and improved version of that described in (Acebo et al., 1994). The earlier version had two main flaws: it was not transportable, and it was too slow to enable massive text ..."
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Cited by 33 (6 self)
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We present in this paper a fast, broadcoverage, accurate morphological analyzer for Spanish words, MACO+, which is an extended and improved version of that described in (Acebo et al., 1994). The earlier version had two main flaws: it was not transportable, and it was too slow to enable massive text processing. The presented system not only overcomes those two flaws, but also offers improved coverage and accuracy. We also present two general partofspeech taggers, which can be used to disambiguate the output of the morphological analyzer. All modules run in any Unix/Linux machine as a pipeline process and they may also be used inside the GATE environment for NLP (Cunningham et al., 1996). The system is currently being used to annotate the LexEsp corpus, a 5.5 million word corpus of Spanish, in a bootstrapping refining procedure. Initial evaluation and results are reported. Keywords: Morphological analysis, corpus linguistics, POS tagging, linguistic resources. 1 Introduction and Mot...
Relaxation Labeling Networks for the Maximum Clique Problem
 J. Artif. Neural Networks
, 1995
"... this paper, it is shown how to take advantage of the properties of these models to approximately solve the maximum clique problem, a wellknown intractable optimization problem which has practical applications in various fields. The approach is based on a result by Motzkin and Straus which naturally ..."
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Cited by 28 (17 self)
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this paper, it is shown how to take advantage of the properties of these models to approximately solve the maximum clique problem, a wellknown intractable optimization problem which has practical applications in various fields. The approach is based on a result by Motzkin and Straus which naturally leads to formulate the problem in a manner that is readily mapped onto a relaxation labeling network. Extensive simulations have demonstrated the validity of the proposed model, both in terms of quality of solutions and speed. Maximum clique problem, relaxation labeling processes, neural networks, optimization. 1 INTRODUCTION
Toward automatic phenotyping of developing embryos from videos
 IEEE Trans. Image Proc
, 2005
"... Abstract — We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was designed as the central component of a fullyautomated phenotyping system. The system contai ..."
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Cited by 23 (8 self)
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Abstract — We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was designed as the central component of a fullyautomated phenotyping system. The system contains three modules (1) a convolutional network trained to classify each pixel into five categories: cell wall, cytoplasm, nucleus membrane, nucleus, outside medium; (2) an EnergyBased Model which cleans up the output of the convolutional network by learning local consistency constraints that must be satisfied by label images; (3) A set of elastic models of the embryo at various stages of development that are matched to the label images. Index Terms — image segmentation, convolutional networks, nonlinear filter, energybased model A. Automatic Phenotyping I.
Continuoustime Relaxation Labeling Processes
, 1998
"... We study the properties of two new relaxation labeling schemes described in terms of differential equations, and hence evolving in countinuous time. This contrasts with the customary approach to defining relaxation labeling algorithms which prefers discrete time. Continuoustime dynamical systems ar ..."
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Cited by 20 (4 self)
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We study the properties of two new relaxation labeling schemes described in terms of differential equations, and hence evolving in countinuous time. This contrasts with the customary approach to defining relaxation labeling algorithms which prefers discrete time. Continuoustime dynamical systems are particularly attractive because they can be implemented directly in hardware circuitry, and the study of their dynamical properties is simpler and more elegant. They are also more plausible as models of biological visual computation. We prove that the proposed models enjoy exactly the same dynamical properties as the classical relaxation labeling schemes, and show how they are intimately related to Hummel and Zucker's now classical theory of constraint satisfaction. In particular, we prove that, when a certain symmetry condition is met, the dynamical systems' behavior is governed by a Liapunov function which turns out to be (the negative of) a wellknown consistency measure. Moreover, we p...
Learning Solution Preferences in Constraint Problems
, 1998
"... Usually, not all the solutions of a finite domain constraint problem (CSP) are equally desirable: some of them may be preferred to others. However, classical CSPs do not allow for this more informative kind of knowledge representation. On the other hand, semiringbased CSPs (SCSPs), where a value is ..."
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Cited by 16 (11 self)
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Usually, not all the solutions of a finite domain constraint problem (CSP) are equally desirable: some of them may be preferred to others. However, classical CSPs do not allow for this more informative kind of knowledge representation. On the other hand, semiringbased CSPs (SCSPs), where a value is associated with each tuple in each constraint, generate solutions with a corresponding value attached, which can be interpreted as the level of preference of that solution. Sometimes, however, even standard SCSPs are not enough, since one may know his/her preferences over some of the solutions but have no idea on how to code this knowledge into the SCSP. In this paper we consider this situation and propose to address it by first defining a classical CSP and giving some examples of solution preferences, and then learning the corresponding SCSP which behaves as the initial CSP (that is, it has the same solutions) and matches the preferences specified in the examples. In other words, we use th...