LEADER 06330nam 22008055 450 001 996465897603316 005 20200630015504.0 010 $a3-540-45028-9 024 7 $a10.1007/3-540-45028-9 035 $a(CKB)1000000000212078 035 $a(SSID)ssj0000323586 035 $a(PQKBManifestationID)11259104 035 $a(PQKBTitleCode)TC0000323586 035 $a(PQKBWorkID)10300787 035 $a(PQKB)10689063 035 $a(DE-He213)978-3-540-45028-3 035 $a(MiAaPQ)EBC3072288 035 $a(PPN)155177036 035 $a(EXLCZ)991000000000212078 100 $a20121227d2003 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aGraph Based Representations in Pattern Recognition$b[electronic resource] $e4th IAPR International Workshop, GbRPR 2003, York, UK, June 30 - July 2, 2003. Proceedings /$fedited by Edwin Hancock, Mario Vento 205 $a1st ed. 2003. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2003. 215 $a1 online resource (VIII, 276 p.) 225 1 $aLecture Notes in Computer Science,$x0302-9743 ;$v2726 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-40452-X 320 $aIncludes bibliographical references and index. 327 $aData Structures and Representation -- Construction of Combinatorial Pyramids -- On Graphs with Unique Node Labels -- Constructing Stochastic Pyramids by MIDES ? Maximal Independent Directed Edge Set -- Segmentation -- Functional Modeling of Structured Images -- Building of Symbolic Hierarchical Graphs for Feature Extraction -- Comparison and Convergence of Two Topological Models for 3D Image Segmentation -- Graph Edit Distance -- Tree Edit Distance from Information Theory -- Self-Organizing Graph Edit Distance -- Graph Edit Distance with Node Splitting and Merging, and Its Application to Diatom Identification -- Graph Matching -- Orthonormal Kernel Kronecker Product Graph Mdatching -- Theoretical Analysis and Experimental Comparison of Graph Matching Algorithms for Database Filtering -- A Comparison of Three Maximum Common Subgraph Algorithms on a Large Database of Labeled Graphs -- Swap Strategies for Graph Matching -- Matrix Methods -- Graph Matching Using Spectral Seriation and String Edit Distance -- Graph Polynomials, Principal Pivoting, and Maximum Independent Sets -- Graph Partition for Matching -- Graph Clustering -- Spectral Clustering of Graphs -- Comparison of Distance Measures for Graph-Based Clustering of Documents -- Some Experiments on Clustering a Set of Strings -- A New Median Graph Algorithm -- Graph Clustering Using the Weighted Minimum Common Supergraph -- ACM Attributed Graph Clustering for Learning Classes of Images -- A Competitive Winner-Takes-All Architecture for Classification and Pattern Recognition of Structures. 330 $aThis volume contains the papers presented at the Fourth IAPR Workshop on Graph Based Representations in Pattern Recognition. The workshop was held at the King?s Manor in York, England between 30 June and 2nd July 2003. The previous workshops in the series were held in Lyon, France (1997), Haindorf, Austria (1999), and Ischia, Italy (2001). The city of York provided an interesting venue for the meeting. It has been said that the history of York is the history of England. There have been both Roman and Viking episodes. For instance, Constantine was proclaimed emperor in York. The city has also been a major seat of ecclesiastical power and was also involved in the development of the railways in the nineteenth century. Much of York?s history is evidenced by its buildings, and the King?s Manor is one of the most important and attractive of these. Originally part of the Abbey, after the dissolution of the monasteries by Henry VIII, the building became a center of government for the Tudors and the Stuarts (who stayed here regularly on their journeys between London and Edinburgh), serving as the headquarters of the Council of the North until it was disbanded in 1561. The building became part of the University of York at its foundation in 1963. The papers in the workshop span the topics of representation, segmentation, graph-matching, graph edit-distance, matrix and spectral methods, and gra- clustering. 410 0$aLecture Notes in Computer Science,$x0302-9743 ;$v2726 606 $aPattern recognition 606 $aComputer science 606 $aData structures (Computer science) 606 $aComputer science?Mathematics 606 $aComputer graphics 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aScience, Humanities and Social Sciences, multidisciplinary$3https://scigraph.springernature.com/ontologies/product-market-codes/A11007 606 $aComputer Science, general$3https://scigraph.springernature.com/ontologies/product-market-codes/I00001 606 $aData Structures$3https://scigraph.springernature.com/ontologies/product-market-codes/I15017 606 $aDiscrete Mathematics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17028 606 $aComputer Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22013 615 0$aPattern recognition. 615 0$aComputer science. 615 0$aData structures (Computer science). 615 0$aComputer science?Mathematics. 615 0$aComputer graphics. 615 14$aPattern Recognition. 615 24$aScience, Humanities and Social Sciences, multidisciplinary. 615 24$aComputer Science, general. 615 24$aData Structures. 615 24$aDiscrete Mathematics in Computer Science. 615 24$aComputer Graphics. 676 $a006.4/2 702 $aHancock$b Edwin$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aVento$b Mario$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 02$aInternational Association for Pattern Recognition, 712 12$aGbRPR 2003 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465897603316 996 $aGraph-Based Representations in Pattern Recognition$9772063 997 $aUNISA