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Author Jon Almazan; David Fernandez; Alicia Fornes; Josep Llados; Ernest Valveny edit   pdf
doi  isbn
openurl 
  Title A Coarse-to-Fine Approach for Handwritten Word Spotting in Large Scale Historical Documents Collection Type Conference Article
  Year 2012 Publication 13th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal  
  Volume Issue Pages 453-458  
  Keywords  
  Abstract In this paper we propose an approach for word spotting in handwritten document images. We state the problem from a focused retrieval perspective, i.e. locating instances of a query word in a large scale dataset of digitized manuscripts. We combine two approaches, namely one based on word segmentation and another one segmentation-free. The first approach uses a hashing strategy to coarsely prune word images that are unlikely to be instances of the query word. This process is fast but has a low precision due to the errors introduced in the segmentation step. The regions containing candidate words are sent to the second process based on a state of the art technique from the visual object detection field. This discriminative model represents the appearance of the query word and computes a similarity score. In this way we propose a coarse-to-fine approach achieving a compromise between efficiency and accuracy. The validation of the model is shown using a collection of old handwritten manuscripts. We appreciate a substantial improvement in terms of precision regarding the previous proposed method with a low computational cost increase.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor (up)  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-1-4673-2262-1 Medium  
  Area Expedition Conference ICFHR  
  Notes DAG Approved no  
  Call Number DAG @ dag @ AFF2012 Serial 1983  
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Author Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny edit   pdf
url  isbn
openurl 
  Title Efficient Exemplar Word Spotting Type Conference Article
  Year 2012 Publication 23rd British Machine Vision Conference Abbreviated Journal  
  Volume Issue Pages 67.1- 67.11  
  Keywords  
  Abstract In this paper we propose an unsupervised segmentation-free method for word spotting in document images.
Documents are represented with a grid of HOG descriptors, and a sliding window approach is used to locate the document regions that are most similar to the query. We use the exemplar SVM framework to produce a better representation of the query in an unsupervised way. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage.		  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor (up)  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 1-901725-46-4 Medium  
  Area Expedition Conference BMVC  
  Notes DAG Approved no  
  Call Number DAG @ dag @ AGF2012 Serial 1984  
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Author Jaume Gibert; Ernest Valveny; Horst Bunke edit   pdf
doi  openurl
  Title Graph Embedding in Vector Spaces by Node Attribute Statistics Type Journal Article
  Year 2012 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 45 Issue 9 Pages 3072-3083  
  Keywords Structural pattern recognition; Graph embedding; Data clustering; Graph classification  
  Abstract Graph-based representations are of broad use and applicability in pattern recognition. They exhibit, however, a major drawback with regards to the processing tools that are available in their domain. Graphembedding into vectorspaces is a growing field among the structural pattern recognition community which aims at providing a feature vector representation for every graph, and thus enables classical statistical learning machinery to be used on graph-based input patterns. In this work, we propose a novel embedding methodology for graphs with continuous nodeattributes and unattributed edges. The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. We specifically deal with an important issue of this methodology, namely, the selection of a suitable set of representatives. In an experimental evaluation, we empirically show the advantages of this novel approach in the context of different classification problems using several databases of graphs.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor (up)  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0031-3203 ISBN Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number Admin @ si @ GVB2012a Serial 1992  
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Author Jaume Gibert; Ernest Valveny; Horst Bunke edit   pdf
doi  openurl
  Title Feature Selection on Node Statistics Based Embedding of Graphs Type Journal Article
  Year 2012 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 33 Issue 15 Pages 1980–1990  
  Keywords Structural pattern recognition; Graph embedding; Feature ranking; PCA; Graph classification  
  Abstract Representing a graph with a feature vector is a common way of making statistical machine learning algorithms applicable to the domain of graphs. Such a transition from graphs to vectors is known as graphembedding. A key issue in graphembedding is to select a proper set of features in order to make the vectorial representation of graphs as strong and discriminative as possible. In this article, we propose features that are constructed out of frequencies of node label representatives. We first build a large set of features and then select the most discriminative ones according to different ranking criteria and feature transformation algorithms. On different classification tasks, we experimentally show that only a small significant subset of these features is needed to achieve the same classification rates as competing to state-of-the-art methods.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor (up)  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number Admin @ si @ GVB2012b Serial 1993  
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Author Sophie Wuerger; Kaida Xiao; Dimitris Mylonas; Q. Huang; Dimosthenis Karatzas; Galina Paramei edit  url
doi  openurl
  Title Blue green color categorization in mandarin english speakers Type Journal Article
  Year 2012 Publication Journal of the Optical Society of America A Abbreviated Journal JOSA A  
  Volume 29 Issue 2 Pages A102-A1207  
  Keywords  
  Abstract Observers are faster to detect a target among a set of distracters if the targets and distracters come from different color categories. This cross-boundary advantage seems to be limited to the right visual field, which is consistent with the dominance of the left hemisphere for language processing [Gilbert et al., Proc. Natl. Acad. Sci. USA 103, 489 (2006)]. Here we study whether a similar visual field advantage is found in the color identification task in speakers of Mandarin, a language that uses a logographic system. Forty late Mandarin-English bilinguals performed a blue-green color categorization task, in a blocked design, in their first language (L1: Mandarin) or second language (L2: English). Eleven color singletons ranging from blue to green were presented for 160 ms, randomly in the left visual field (LVF) or right visual field (RVF). Color boundary and reaction times (RTs) at the color boundary were estimated in L1 and L2, for both visual fields. We found that the color boundary did not differ between the languages; RTs at the color boundary, however, were on average more than 100 ms shorter in the English compared to the Mandarin sessions, but only when the stimuli were presented in the RVF. The finding may be explained by the script nature of the two languages: Mandarin logographic characters are analyzed visuospatially in the right hemisphere, which conceivably facilitates identification of color presented to the LVF.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor (up)  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number Admin @ si @ WXM2012 Serial 2007  
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Author Yunchao Gong; Svetlana Lazebnik; Albert Gordo; Florent Perronnin edit   pdf
doi  isbn
openurl 
  Title Iterative quantization: A procrustean approach to learning binary codes for Large-Scale Image Retrieval Type Journal Article
  Year 2012 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 35 Issue 12 Pages 2916-2929  
  Keywords  
  Abstract This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, dubbed iterative quantization (ITQ), has connections to multi-class spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or “classemes” on the ImageNet dataset.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor (up)  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0162-8828 ISBN 978-1-4577-0394-2 Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number Admin @ si @ GLG 2012b Serial 2008  
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Author Dimosthenis Karatzas;Ch. Lioutas edit  openurl
  Title Software Package Development for Electron Diffraction Image Analysis Type Conference Article
  Year 1998 Publication Proceedings of the XIV Solid State Physics National Conference Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Ioannina, Greece  
  Corporate Author Thesis  
  Publisher Place of Publication Editor (up)  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number IAM @ iam @ KaL1998 Serial 2045  
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Author Albert Gordo; Florent Perronnin; Ernest Valveny edit   pdf
doi  isbn
openurl 
  Title Document classification using multiple views Type Conference Article
  Year 2012 Publication 10th IAPR International Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages 33-37  
  Keywords  
  Abstract The combination of multiple features or views when representing documents or other kinds of objects usually leads to improved results in classification (and retrieval) tasks. Most systems assume that those views will be available both at training and test time. However, some views may be too `expensive' to be available at test time. In this paper, we consider the use of Canonical Correlation Analysis to leverage `expensive' views that are available only at training time. Experimental results show that this information may significantly improve the results in a classification task.  
  Address Australia  
  Corporate Author Thesis  
  Publisher IEEE Computer Society Washington Place of Publication Editor (up)  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-0-7695-4661-2 Medium  
  Area Expedition Conference DAS  
  Notes DAG Approved no  
  Call Number Admin @ si @ GPV2012 Serial 2049  
Permanent link to this record
 
 
Author Albert Gordo; Jose Antonio Rodriguez; Florent Perronnin; Ernest Valveny edit   pdf
doi  isbn
openurl 
  Title Leveraging category-level labels for instance-level image retrieval Type Conference Article
  Year 2012 Publication 25th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 3045-3052  
  Keywords  
  Abstract In this article, we focus on the problem of large-scale instance-level image retrieval. For efficiency reasons, it is common to represent an image by a fixed-length descriptor which is subsequently encoded into a small number of bits. We note that most encoding techniques include an unsupervised dimensionality reduction step. Our goal in this work is to learn a better subspace in a supervised manner. We especially raise the following question: “can category-level labels be used to learn such a subspace?” To answer this question, we experiment with four learning techniques: the first one is based on a metric learning framework, the second one on attribute representations, the third one on Canonical Correlation Analysis (CCA) and the fourth one on Joint Subspace and Classifier Learning (JSCL). While the first three approaches have been applied in the past to the image retrieval problem, we believe we are the first to show the usefulness of JSCL in this context. In our experiments, we use ImageNet as a source of category-level labels and report retrieval results on two standard dataseis: INRIA Holidays and the University of Kentucky benchmark. Our experimental study shows that metric learning and attributes do not lead to any significant improvement in retrieval accuracy, as opposed to CCA and JSCL. As an example, we report on Holidays an increase in accuracy from 39.3% to 48.6% with 32-dimensional representations. Overall JSCL is shown to yield the best results.  
  Address Providence, Rhode Island  
  Corporate Author Thesis  
  Publisher IEEE Xplore Place of Publication Editor (up)  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1063-6919 ISBN 978-1-4673-1226-4 Medium  
  Area Expedition Conference CVPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ GRP2012 Serial 2050  
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Author Francisco Cruz; Oriol Ramos Terrades edit   pdf
url  openurl
  Title Document segmentation using relative location features Type Conference Article
  Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 1562-1565  
  Keywords  
  Abstract In this paper we evaluate the use of Relative Location Features (RLF) on a historical document segmentation task, and compare the quality of the results obtained on structured and unstructured documents using RLF and not using them. We prove that using these features improve the final segmentation on documents with a strong structure, while their application on unstructured documents does not show significant improvement. Although this paper is not focused on segmenting unstructured documents, results obtained on a benchmark dataset are equal or even overcome previous results of similar works.  
  Address Tsukuba Science City, Japan  
  Corporate Author Thesis  
  Publisher Place of Publication Editor (up)  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ CrR2012 Serial 2051  
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