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16th International Conference on Frontiers in Handwriting recognition (ICFHR)

作者： Maergner, Paul Howe, Nicholas R. Riesen, Kaspar Ingold, Rolf Fischer, Andreas Univ Fribourg Dept Informat Fribourg Switzerland Univ Appl Sci & Arts Western Switzerland Inst Complex Syst Fribourg Switzerland Univ Appl Sci & Arts Northwestern Switzerland Inst Informat Syst Olten Switzerland Smith Coll Dept Comp Sci Northampton MA 01063 USA

ISBN: (纸本)9781538658758

For handwritten signature verification, signature images are typically represented with fixed-sized feature vectors capturing local and global properties of the handwriting. Graph-based representations offer a promising alternative, as they are flexible in size and model the global structure of the handwriting. However, they are only rarely used for signature verification, which may be due to the high computational complexity involved when matching two graphs. In this paper, we take a closer look at two recently presented structural methods for handwriting analysis, for which efficient matching methods are available: keypoint graphs with approximate graph edit distance and inkball models. Inkball models, in particular, have never been used for signature verification before. We investigate both approaches individually and propose a combined verification system, which demonstrates an excellent performance on the MCYT and GPDS benchmark data sets when compared with the state of the art.

关键词： offline signature verification structural pattern recognition graph edit distance inkball models

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Speeding up Graph Matching by Means of Systematic Graph Reductions Using Centrality Measures 12

Speeding up Graph Matching by Means of Systematic Graph Redu...

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12th International Conference on pattern recognition Systems (ICPRS)

作者： Gillioz, Anthony Riesen, Kaspar Univ Bern Inst Comp Sci Bern Switzerland

ISBN: (数字)9781665466943

ISBN: (纸本)9781665466943

Comparing large graphs using graph edit distance is a time-consuming or even intractable problem. This is mainly due to the exponential time complexity of graph edit distance w.r.t. to the size of the underlying graphs. Nevertheless, considering its great adaptability, graph edit distance is a popular metric in structural pattern recognition and has found widespread applications. Main contribution of the present paper is to substantially speed up the computation of graph edit distance by initially reducing the graphs to the most important substructures. The graph downsizing is based on centrality measures - e.g. PageRank -. That is, we systematically delete nodes including their edges from our graphs that offer the least level of influence to the overall graph structure. We empirically validate the benefit of our reduction method by classifying reduced graphs from a broad range of applications with a distance-based classifier. The main finding is that we can reduce the computation time by a wide margin while maintaining satisfying classification accuracy.

关键词： structural pattern recognition Graph Matching Centrality Measures

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Segmentation and preliminary recognition of madrigals notated in white mensural notation

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Machine Vision and Applications: An International Journal 1992年第3期5卷 223-229页

作者： Carter, Nicholas P. Departments of Physics and Music University of Surrey Guildford Surrey GU2 5XH United Kingdom

An automatic music score-reading system will facilitate applications including computer-based editing of new editions, production of databases for musicological research, and creation of braille or large-format scores for the blind or partially-sighted. The work described here deals specifically with initial processing of images containing early seventeenth century madrigals notated in white mensural notation. The problems of segmentation involved in isolating the musical symbols from the word-underlay and decorative graphics are compounded by the poor quality of the originals which present a significant challenge to any recognition system. The solution described takes advantage of structural decomposition techniques based on a novel transformation of the line adjacency graph which have been developed during work on a score-reading system for conventional music notation. © 1992, Springer-Verlag New York Inc.. All rights reserved.

关键词： printed music structural pattern recognition white mensural notation

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Fuzzy spatial relationships for image processing and interpretation: a review

Fuzzy spatial relationships for image processing and interpr...

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11th International Conference on Discrete Geometry for Computer Imagery

作者： Bloch, I LTCI CNRS UMR 5141 Ecole Natl Super TelecommunDept TSI F-75013 Paris France

In spatial reasoning, relationships between spatial entities play a major role. In image interpretation, computer vision and structural recognition, the management of imperfect information and of imprecision constitutes a key point. This calls for the framework of fuzzy sets, which exhibits nice features to represent spatial imprecision at different levels, imprecision in knowledge and knowledge representation, and which provides powerful tools for fusion, decision-making and reasoning. In this paper, we review the main fuzzy approaches for defining spatial relationships including topological (set relationships, adjacency) and metrical relations (distances, directional relative position). (C) 2004 Elsevier B.V. All rights reserved.

关键词： fuzzy spatial relationships degree of intersection degree of inclusion degree of adjacency distances directional relative position structural pattern recognition image interpretation spatial reasoning

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Graph database filtering using decision trees

Graph database filtering using decision trees

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17th International Conference on pattern recognition (ICPR)

作者： Irniger, C Bunke, H Univ Bern Dept Comp Sci CH-3012 Bern Switzerland

ISBN: (纸本)0769521282

Graphs are a powerful representation formalism for structural data. They are, however very expensive from the computational point of view. In pattern recognition it is often necessary to match an unknown sample against a database of candidate patterns. In this process, however the size of the database is introduced as an additional factor into the overall complexity of the matching process. To reduce the influence of that factor an approach based on machine learning techniques is proposed in this paper Graphs are represented using feature vectors. Based on these vectors a decision tree is built to index the database. The decision tree allows at runtime to eliminate a number of graphs from the database as possible matching candidates.

关键词： structural pattern recognition graph matching graph-isomorphism subgraph-isomorphism database filtering decision trees machine learning

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A Multi-agent Approach for Graph Classification 13th

A Multi-agent Approach for Graph Classification

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13th International Joint Conference on Computational Intelligence (IJCCI) / 13th International Conference on Evolutionary Computation Theory and Applications (ECTA)

作者： Baldini, Luca Rizzi, Antonello Univ Roma La Sapienza Dept Informat Engn Elect & Telecommun Via Eudossiana 18 I-00184 Rome Italy

ISBN: (纸本)9789897585340

In this paper, we propose and discuss a prototypical framework for graph classification. The proposed algorithm (Graph E-ABC) exploits a multi-agent design, where swarm of agents (orchestrated via evolutionary optimization) are in charge of finding meaningful substructures from the training data. The resulting set of substructures compose the pivotal entities for a graph embedding procedure that allows to move the pattern recognition problem from the graph domain towards the Euclidean space. In order to improve the learning capabilities, the pivotal substructures undergo an independent optimization procedure. The performances of Graph E-ABC are addressed via a sensitivity analysis over its critical parameters and compared against current approaches for graph classification. Results on five open access datasets of fully labelled graphs show interesting performances in terms of accuracy, counterbalanced by a relatively high number of pivotal substructures.

关键词： Multi-agent Systems Graph Embedding Supervised Learning structural pattern recognition

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New techniques and technologies for information retrieval and knowledge extraction from nuclear fusion massive databases

New techniques and technologies for information retrieval an...

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IEEE International Symposium on Intelligent Signal Processing

作者： Murari, A. Vega, J. Alonso, J. A. De La Luna, E. Farthing, J. Hidalgo, C. Ratt, G. A. Svensson, J. Vagliasindi, G. EURATOM ENEA Fus Consorzio RFX I-35127 Padua Italy CIEMAT Assoc EURATOM CIEMA4Tpara Fusion Madrid Spain Culham Sci Ctr EURATOM/UKAEA Fusion Assoc Abingdon Oxon England Teilinstitut Greifswald EURATOM Assoc D-17491 Greifswald Germany

ISBN: (纸本)9781424408290

Reactor relevant experiments for Magnetic Confinement Fusion, like JET, produce already tens of GBytes of data per shot and the next step device, ITER. is expected to require orders of magnitude more. Managing such vast quantities of data in an efficient way needs new techniques, ranging from signal storage and information retrieval to data analysis for physical interpretation. At JET significant efforts are being devoted to all the main issues. Lossless data compression is under development for both mono and bi-dimensional signals, together with new techniques and technologies for image processing (directional wavelets and Cellular Non-linear Networks). structural pattern recognition has shown great potential for information retrieval. Statistical methods, like Bayesian inference and regression trees, are being systematically investigated, to extract the required knowledge from all the available measurements. Other Soft Computing techniques, like Fuzzy Logic and Artificial Neural Networks, are very powerful tools to handle the great complexity and uncertainties of present day and near Wore experiments.

关键词： Bayesian statistics CART data compression fuzzy logic real time structural pattern recognition

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Tensor-based Total Bregman Divergences between Graphs

Tensor-based Total Bregman Divergences between Graphs

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IEEE International Conference on Computer Vision (ICCV)

作者： Escolano, Francisco Liu, Meizhu Hancock, Edwin R. Univ Alicante Alicante Spain Univ Florida Gainesville FL 32611 USA York Univ N York ON M3J 1P3 Canada

ISBN: (纸本)9781467300636

The accurate and effective measurement of graph-similarity has proved to be a challenging problem in structural pattern recognition. In this paper we extend the node coverage approach for graph indexing, which outperforms entropic manifold alignment. The proposed extension relies on replacing the Henze-Penrose divergence by a Total Bregman Divergence (TBD) which relies on error free distances (Frobenius norms) for the tensors of the common tangent space. To that end we exploit linear combinations of Gaussians which can be computed faster than the minimum spanning trees needed for obtaining the Henze-Penrose divergence. In the paper we also propose several divergences for these variables (linear combinations): Jeffreys TBD, Jensen-Shannon TBD and Jensen-Renyi. In our experiments we show that all of these divergences are highly discriminative: all of them improve the retrieval-recall results obtained with the Henze-Penrose divergence within the node coverage approach, being the Jeffreys TBD divergence the best.

关键词： structural pattern recognition Linear Combination Gauss Tensor common tangent Coverage

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Graph Based K-Nearest Neighbor Minutiae Clustering for Fingerprint recognition 10

Graph Based K-Nearest Neighbor Minutiae Clustering for Finge...

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10th International Conference on Natural Computation (ICNC)

作者： Pawar, Vaishali Zaveri, Mukesh NDMVP Ss KBT Coll Engn Dept Informat Technol Nasik Maharashtra India SVNIT Dept Comp Engn Surat Gujarat India

ISBN: (纸本)9781479951512

The graph is an efficient data structure to represent multi-dimensional data and their complex relations. pattern matching and data mining are the two important fields of computer science. pattern matching finds a particular pattern in the given input where as data mining deals with selecting specific data from the huge databases. This work contributes towards the combination of graph theory, pattern recognition and graph based databases. A variety of graph based techniques have been proposed as a powerful tool for pattern representation and classification in the past years. For a longer time graphs remained computationally expensive tool. But recently the graph based structural pattern recognition and image processing is becoming popular. The computational complexity of the graph based methods is becoming feasible due to high end new generations of the computers and the research advancements. In this work we have implemented graph based fingerprint recognition algorithm. The fingerprints are represented as attributed relational graphs. In the pattern recognition phase graph matching is applied. This study focuses on the clustering of graph databases prior to graph matching. When the structural feature set size of the data grows longer, graph matching becomes expensive. The clustering of graph databases drastically reduce the graph matching candidates.

关键词： Clustering algorithms Databases Feature extraction Fingerprint recognition Image edge detection Syntactics Fingerprint recognition Edge detection Clustering algorithms Database Graph matching Feature extraction Line graph dimensional data graphic library structural pattern recognition based

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Graph based Method for Online Handwritten Character recognition 15

Graph based Method for Online Handwritten Character Recognit...

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15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 15th International Conference on Computer Vision Theory and Applications (VISAPP)

作者： Zitouni, Rabiaa Bezine, Hala Arous, Najet Univ Tunis El Manar Lab LR SITI ENIT BP 37 Tunis 1002 Tunisia Univ Sfax Lab REGIM ENIS BP 1173 Sfax 3038 Tunisia

ISBN: (纸本)9789897584022

In this research, we attempt to propose a novel graph-based approach for online handwritten character recognition. Unlike the most well-known online handwritten recognition methods, which are based on statistical representations, we set forward a new approach based on structural representation to overcome the inherent deformations of handwritten characters. An Attributed Relational Graph (ARG) is dedicated to allowing the direct labeling of nodes (strokes) and edges (relationships) of a graph to model the input character. Each node is characterized by a set of fuzzy membership degrees describing their properties (type, size). Fuzzy description is invested in order to guarantee more robustness against uncertainty, ambiguity and vagueness. ARGs edges stand for spatial relationships between different strokes. At a subsequent stage, a tree-search based optimal matching algorithm is explored, which allows the search for character structures i.e the minimum cost of nodes. Experiments performed on ADAB and IRONOFF datasets, reveal promising results. In particular, the comparison with the state of the art demonstrates the significance of the proposed system.

关键词： Fuzzy Attributed Relational Graph Graph Matching structural pattern recognition Handwritten Graphs Tree Search Method

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