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Author Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal edit   pdf
url  openurl
  Title Evaluation of the Effect of Improper Segmentation on Word Spotting Type Journal Article
  Year (down) 2019 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR  
  Volume 22 Issue Pages 361-374  
  Keywords  
  Abstract Word spotting is an important recognition task in large-scale retrieval of document collections. In most of the cases, methods are developed and evaluated assuming perfect word segmentation. In this paper, we propose an experimental framework to quantify the goodness that word segmentation has on the performance achieved by word spotting methods in identical unbiased conditions. The framework consists of generating systematic distortions on segmentation and retrieving the original queries from the distorted dataset. We have tested our framework on several established and state-of-the-art methods using George Washington and Barcelona Marriage Datasets. The experiments done allow for an estimate of the end-to-end performance of word spotting methods.  
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  Notes DAG; 600.097; 600.084; 600.121; 600.140; 600.129 Approved no  
  Call Number Admin @ si @ DNL2019 Serial 3455  
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Author Thanh Ha Do; Oriol Ramos Terrades; Salvatore Tabbone edit  url
openurl 
  Title DSD: document sparse-based denoising algorithm Type Journal Article
  Year (down) 2019 Publication Pattern Analysis and Applications Abbreviated Journal PAA  
  Volume 22 Issue 1 Pages 177–186  
  Keywords Document denoising; Sparse representations; Sparse dictionary learning; Document degradation models  
  Abstract In this paper, we present a sparse-based denoising algorithm for scanned documents. This method can be applied to any kind of scanned documents with satisfactory results. Unlike other approaches, the proposed approach encodes noise documents through sparse representation and visual dictionary learning techniques without any prior noise model. Moreover, we propose a precision parameter estimator. Experiments on several datasets demonstrate the robustness of the proposed approach compared to the state-of-the-art methods on document denoising.  
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  Notes DAG; 600.097; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ DRT2019 Serial 3254  
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Author Anjan Dutta; Hichem Sahbi edit   pdf
doi  openurl
  Title Stochastic Graphlet Embedding Type Journal Article
  Year (down) 2018 Publication IEEE Transactions on Neural Networks and Learning Systems Abbreviated Journal TNNLS  
  Volume Issue Pages 1-14  
  Keywords Stochastic graphlets; Graph embedding; Graph classification; Graph hashing; Betweenness centrality  
  Abstract Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments,
etc.) and edges characterize the relationships between these primitives. However, these non-vectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of – explicit/implicit –graph vectorization and embedding. This embedding process
should be resilient to intra-class graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding (SGE) that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider
these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution of these graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When
combined with maximum margin classifiers, these graphlet-based representations have positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases.
 
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  Notes DAG; 602.167; 602.168; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ DuS2018 Serial 3225  
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Author Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal edit   pdf
url  openurl
  Title Product graph-based higher order contextual similarities for inexact subgraph matching Type Journal Article
  Year (down) 2018 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 76 Issue Pages 596-611  
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  Abstract Many algorithms formulate graph matching as an optimization of an objective function of pairwise quantification of nodes and edges of two graphs to be matched. Pairwise measurements usually consider local attributes but disregard contextual information involved in graph structures. We address this issue by proposing contextual similarities between pairs of nodes. This is done by considering the tensor product graph (TPG) of two graphs to be matched, where each node is an ordered pair of nodes of the operand graphs. Contextual similarities between a pair of nodes are computed by accumulating weighted walks (normalized pairwise similarities) terminating at the corresponding paired node in TPG. Once the contextual similarities are obtained, we formulate subgraph matching as a node and edge selection problem in TPG. We use contextual similarities to construct an objective function and optimize it with a linear programming approach. Since random walk formulation through TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities and better discrimination among the nodes and edges. Experimental results shown on synthetic as well as real benchmarks illustrate that higher order contextual similarities increase discriminating power and allow one to find approximate solutions to the subgraph matching problem.  
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  Notes DAG; 602.167; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ DLB2018 Serial 3083  
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Author Fernando Vilariño; Dimosthenis Karatzas; Alberto Valcarce edit  openurl
  Title The Library Living Lab Barcelona: A participative approach to technology as an enabling factor for innovation in cultural spaces Type Journal
  Year (down) 2018 Publication Technology Innovation Management Review Abbreviated Journal  
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  Notes DAG; MV; 600.097; 600.121; 600.129;SIAI Approved no  
  Call Number Admin @ si @ VKV2018a Serial 3153  
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