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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  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number Admin @ si @ GVB2012b Serial 1993  
Permanent link to this record
 
 
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  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  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  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  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  
Permanent link to this record
 
 
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  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number IAM @ iam @ KaL1998 Serial 2045  
Permanent link to this record
 
 
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  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  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  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  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  
Permanent link to this record
 
 
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  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ CrR2012 Serial 2051  
Permanent link to this record
 
 
Author Volkmar Frinken; Francisco Zamora; Salvador España; Maria Jose Castro; Andreas Fischer; Horst Bunke edit   pdf
isbn  openurl
  Title Long-Short Term Memory Neural Networks Language Modeling for Handwriting Recognition Type Conference Article
  Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 701-704  
  Keywords  
  Abstract Unconstrained handwritten text recognition systems maximize the combination of two separate probability scores. The first one is the observation probability that indicates how well the returned word sequence matches the input image. The second score is the probability that reflects how likely a word sequence is according to a language model. Current state-of-the-art recognition systems use statistical language models in form of bigram word probabilities. This paper proposes to model the target language by means of a recurrent neural network with long-short term memory cells. Because the network is recurrent, the considered context is not limited to a fixed size especially as the memory cells are designed to deal with long-term dependencies. In a set of experiments conducted on the IAM off-line database we show the superiority of the proposed language model over statistical n-gram models.  
  Address Tsukuba Science City, Japan  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN 1051-4651 ISBN 978-1-4673-2216-4 Medium  
  Area Expedition Conference ICPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ FZE2012 Serial 2052  
Permanent link to this record
 
 
Author Marçal Rusiñol; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados edit   pdf
isbn  openurl
  Title Multipage Document Retrieval by Textual and Visual Representations Type Conference Article
  Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 521-524  
  Keywords  
  Abstract In this paper we present a multipage administrative document image retrieval system based on textual and visual representations of document pages. Individual pages are represented by textual or visual information using a bag-of-words framework. Different fusion strategies are evaluated which allow the system to perform multipage document retrieval on the basis of a single page retrieval system. Results are reported on a large dataset of document images sampled from a banking workflow.  
  Address Tsukuba Science City, Japan  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN 1051-4651 ISBN 978-1-4673-2216-4 Medium  
  Area Expedition Conference ICPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ RKB2012 Serial 2053  
Permanent link to this record
 
 
Author Marçal Rusiñol; Josep Llados edit  doi
isbn  openurl
  Title The Role of the Users in Handwritten Word Spotting Applications: Query Fusion and Relevance Feedback Type Conference Article
  Year 2012 Publication 13th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal  
  Volume Issue Pages 55-60  
  Keywords  
  Abstract In this paper we present the importance of including the user in the loop in a handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and a baseline word spotting approach based on a bag-of-visual-words model.  
  Address Bari, Italy  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-1-4673-2262-1 Medium  
  Area Expedition Conference ICFHR  
  Notes DAG Approved no  
  Call Number Admin @ si @ RuL2012 Serial 2054  
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