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Author Oriol Ramos Terrades; Albert Berenguel; Debora Gil edit   pdf
url  openurl
  Title (up) A flexible outlier detector based on a topology given by graph communities Type Miscellaneous
  Year 2020 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings.  
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  Notes IAM; DAG; 600.139; 600.145; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ RBG2020 Serial 3475  
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Author Oriol Ramos Terrades; Albert Berenguel; Debora Gil edit   pdf
doi  openurl
  Title (up) A Flexible Outlier Detector Based on a Topology Given by Graph Communities Type Journal Article
  Year 2022 Publication Big Data Research Abbreviated Journal BDR  
  Volume 29 Issue Pages 100332  
  Keywords Classification algorithms; Detection algorithms; Description of feature space local structure; Graph communities; Machine learning algorithms; Outlier detectors  
  Abstract Outlier detection is essential for optimal performance of machine learning methods and statistical predictive models. Their detection is especially determinant in small sample size unbalanced problems, since in such settings outliers become highly influential and significantly bias models. This particular experimental settings are usual in medical applications, like diagnosis of rare pathologies, outcome of experimental personalized treatments or pandemic emergencies. In contrast to population-based methods, neighborhood based local approaches compute an outlier score from the neighbors of each sample, are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. A main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters, like the number of neighbors.
This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world and synthetic data sets show that our approach outperforms, both, local and global strategies in multi and single view settings.
 
  Address August 28, 2022  
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  Notes DAG; IAM; 600.140; 600.121; 600.139; 600.145; 600.159 Approved no  
  Call Number Admin @ si @ RBG2022a Serial 3718  
Permanent link to this record
 

 
Author Antonio Clavelli; Dimosthenis Karatzas; Josep Llados edit  doi
isbn  openurl
  Title (up) A framework for the assessment of text extraction algorithms on complex colour images Type Conference Article
  Year 2010 Publication 9th IAPR International Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages 19–26  
  Keywords  
  Abstract The availability of open, ground-truthed datasets and clear performance metrics is a crucial factor in the development of an application domain. The domain of colour text image analysis (real scenes, Web and spam images, scanned colour documents) has traditionally suffered from a lack of a comprehensive performance evaluation framework. Such a framework is extremely difficult to specify, and corresponding pixel-level accurate information tedious to define. In this paper we discuss the challenges and technical issues associated with developing such a framework. Then, we describe a complete framework for the evaluation of text extraction methods at multiple levels, provide a detailed ground-truth specification and present a case study on how this framework can be used in a real-life situation.  
  Address Boston; USA;  
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  Series Volume Series Issue Edition  
  ISSN ISBN 978-1-60558-773-8 Medium  
  Area Expedition Conference DAS  
  Notes DAG Approved no  
  Call Number DAG @ dag @ CKL2010 Serial 1432  
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Author Muhammad Muzzamil Luqman; Josep Llados; Jean-Yves Ramel; Thierry Brouard edit  doi
isbn  openurl
  Title (up) A Fuzzy-Interval Based Approach For Explicit Graph Embedding, Recognizing Patterns in Signals, Speech, Images and Video Type Conference Article
  Year 2010 Publication 20th International Conference on Pattern Recognition Abbreviated Journal  
  Volume 6388 Issue Pages 93–98  
  Keywords  
  Abstract We present a new method for explicit graph embedding. Our algorithm extracts a feature vector for an undirected attributed graph. The proposed feature vector encodes details about the number of nodes, number of edges, node degrees, the attributes of nodes and the attributes of edges in the graph. The first two features are for the number of nodes and the number of edges. These are followed by w features for node degrees, m features for k node attributes and n features for l edge attributes — which represent the distribution of node degrees, node attribute values and edge attribute values, and are obtained by defining (in an unsupervised fashion), fuzzy-intervals over the list of node degrees, node attributes and edge attributes. Experimental results are provided for sample data of ICPR2010 contest GEPR.  
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  Publisher Springer, Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-17710-1 Medium  
  Area Expedition Conference ICPR  
  Notes DAG Approved no  
  Call Number DAG @ dag @ LLR2010 Serial 1459  
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Author Ernest Valveny; Philippe Dosch edit  openurl
  Title (up) A general framework for the evaluation of symbol recognition methods Type Journal
  Year 2006 Publication International Journal on Document Analysis and Recognition Abbreviated Journal  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ VaD2006 Serial 686  
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Author Ernest Valveny; Philippe Dosch; Adam Winstanley; Yu Zhou; Su Yang; Luo Yan; Liu Wenyin; Dave Elliman; Mathieu Nicolas Delalandre; Eric Trupin; Sebastien Adam; Jean-Marc Ogier edit  openurl
  Title (up) A general framework for the evaluation of symbol recognition methods Type Journal
  Year 2006 Publication International Journal on Document Analysis and Recognition (IJDAR), 9(1): 59–74 Abbreviated Journal  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ VDW2006 Serial 801  
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Author Ernest Valveny; Philippe Dosch edit  openurl
  Title (up) A General Framework for the Evaluation of Symbol Recognition Methods Type Journal
  Year 2007 Publication International Journal on Document Analysis and Recognition, vol. 9(1), pp 59–74 Abbreviated Journal  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ VaD2007 Serial 893  
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Author Josep Llados; Dimosthenis Karatzas; Joan Mas; Gemma Sanchez edit  openurl
  Title (up) A Generic Architecture for the Conversion of Document Collections into Semantically Annotated Digital Archives Type Journal
  Year 2008 Publication Journal of Universal Computer Science Abbreviated Journal  
  Volume 14 Issue 18 Pages 2912–2935  
  Keywords Median Graph, Graph Embedding, Graph Matching, Structural Pattern Recognition  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ LKM2008 Serial 1142  
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Author Miquel Ferrer; Dimosthenis Karatzas; Ernest Valveny; I. Bardaji; Horst Bunke edit  openurl
  Title (up) A Generic Framework for Median Graph Computation based on a Recursive Embedding Approach Type Journal Article
  Year 2011 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU  
  Volume 115 Issue 7 Pages 919-928  
  Keywords Median Graph, Graph Embedding, Graph Matching, Structural Pattern Recognition  
  Abstract The median graph has been shown to be a good choice to obtain a represen- tative of a set of graphs. However, its computation is a complex problem. Recently, graph embedding into vector spaces has been proposed to obtain approximations of the median graph. The problem with such an approach is how to go from a point in the vector space back to a graph in the graph space. The main contribution of this paper is the generalization of this previ- ous method, proposing a generic recursive procedure that permits to recover the graph corresponding to a point in the vector space, introducing only the amount of approximation inherent to the use of graph matching algorithms. In order to evaluate the proposed method, we compare it with the set me- dian and with the other state-of-the-art embedding-based methods for the median graph computation. The experiments are carried out using four dif- ferent databases (one semi-artificial and three containing real-world data). Results show that with the proposed approach we can obtain better medi- ans, in terms of the sum of distances to the training graphs, than with the previous existing methods.  
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  Notes DAG Approved no  
  Call Number IAM @ iam @ FKV2011 Serial 1831  
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Author Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados edit   pdf
doi  openurl
  Title (up) A Generic Image Retrieval Method for Date Estimation of Historical Document Collections Type Conference Article
  Year 2022 Publication Document Analysis Systems.15th IAPR International Workshop, (DAS2022) Abbreviated Journal  
  Volume 13237 Issue Pages 583–597  
  Keywords Date estimation; Document retrieval; Image retrieval; Ranking loss; Smooth-nDCG  
  Abstract Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others. This paper presents a robust date estimation system based in a retrieval approach that generalizes well in front of heterogeneous collections. We use a ranking loss function named smooth-nDCG to train a Convolutional Neural Network that learns an ordination of documents for each problem. One of the main usages of the presented approach is as a tool for historical contextual retrieval. It means that scholars could perform comparative analysis of historical images from big datasets in terms of the period where they were produced. We provide experimental evaluation on different types of documents from real datasets of manuscript and newspaper images.  
  Address La Rochelle, France; May 22–25, 2022  
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  Notes DAG; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ MGR2022 Serial 3694  
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