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Author (up) Albert Gordo; Alicia Fornes; Ernest Valveny edit   pdf
doi  openurl
  Title Writer identification in handwritten musical scores with bags of notes Type Journal Article
  Year 2013 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 46 Issue 5 Pages 1337-1345  
  Keywords  
  Abstract Writer Identification is an important task for the automatic processing of documents. However, the identification of the writer in graphical documents is still challenging. In this work, we adapt the Bag of Visual Words framework to the task of writer identification in handwritten musical scores. A vanilla implementation of this method already performs comparably to the state-of-the-art. Furthermore, we analyze the effect of two improvements of the representation: a Bhattacharyya embedding, which improves the results at virtually no extra cost, and a Fisher Vector representation that very significantly improves the results at the cost of a more complex and costly representation. Experimental evaluation shows results more than 20 points above the state-of-the-art in a new, challenging dataset.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  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 @ GFV2013 Serial 2307  
Permanent link to this record
 
 
Author (up) Albert Gordo; Alicia Fornes; Ernest Valveny; Josep Llados edit  doi
isbn  openurl
  Title A Bag of Notes Approach to Writer Identification in Old Handwritten Music Scores Type Conference Article
  Year 2010 Publication 9th IAPR International Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages 247–254  
  Keywords  
  Abstract Determining the authorship of a document, namely writer identification, can be an important source of information for document categorization. Contrary to text documents, the identification of the writer of graphical documents is still a challenge. In this paper we present a robust approach for writer identification in a particular kind of graphical documents, old music scores. This approach adapts the bag of visual terms method for coping with graphic documents. The identification is performed only using the graphical music notation. For this purpose, we generate a graphic vocabulary without recognizing any music symbols, and consequently, avoiding the difficulties in the recognition of hand-drawn symbols in old and degraded documents. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving very high identification rates.  
  Address Boston; USA;  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  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 @ GFV2010 Serial 1320  
Permanent link to this record
 
 
Author (up) Albert Gordo; Ernest Valveny edit  doi
isbn  openurl
  Title A rotation invariant page layout descriptor for document classification and retrieval Type Conference Article
  Year 2009 Publication 10th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 481–485  
  Keywords  
  Abstract Document classification usually requires of structural features such as the physical layout to obtain good accuracy rates on complex documents. This paper introduces a descriptor of the layout and a distance measure based on the cyclic dynamic time warping which can be computed in O(n2). This descriptor is translation invariant and can be easily modified to be scale and rotation invariant. Experiments with this descriptor and its rotation invariant modification are performed on the Girona archives database and compared against another common layout distance, the minimum weight edge cover. The experiments show that these methods outperform the MWEC both in accuracy and speed, particularly on rotated documents.  
  Address Barcelona, Spain  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1520-5363 ISBN 978-1-4244-4500-4 Medium  
  Area Expedition Conference ICDAR  
  Notes DAG Approved no  
  Call Number DAG @ dag @ GoV2009a Serial 1175  
Permanent link to this record
 
 
Author (up) Albert Gordo; Ernest Valveny edit  doi
isbn  openurl
  Title The diagonal split: A pre-segmentation step for page layout analysis & classification Type Conference Article
  Year 2009 Publication 4th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal  
  Volume 5524 Issue Pages 290–297  
  Keywords  
  Abstract Document classification is an important task in all the processes related to document storage and retrieval. In the case of complex documents, structural features are needed to achieve a correct classification. Unfortunately, physical layout analysis is error prone. In this paper we present a pre-segmentation step based on a divide & conquer strategy that can be used to improve the page segmentation results, independently of the segmentation algorithm used. This pre-segmentation step is evaluated in classification and retrieval using the selective CRLA algorithm for layout segmentation together with a clustering based on the voronoi area diagram, and tested on two different databases, MARG and Girona Archives.  
  Address Póvoa de Varzim, Portugal  
  Corporate Author Thesis  
  Publisher Springer Berlin 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 978-3-642-02171-8 Medium  
  Area Expedition Conference IbPRIA  
  Notes DAG Approved no  
  Call Number DAG @ dag @ Gov2009b Serial 1176  
Permanent link to this record
 
 
Author (up) Albert Gordo; Florent Perronnin edit  doi
isbn  openurl
  Title A Bag-of-Pages Approach to Unordered Multi-Page Document Classification Type Conference Article
  Year 2010 Publication 20th International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 1920–1923  
  Keywords  
  Abstract We consider the problem of classifying documents containing multiple unordered pages. For this purpose, we propose a novel bag-of-pages document representation. To represent a document, one assigns every page to a prototype in a codebook of pages. This leads to a histogram representation which can then be fed to any discriminative classifier. We also consider several refinements over this initial approach. We show on two challenging datasets that the proposed approach significantly outperforms a baseline system.  
  Address Istanbul (Turkey)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1051-4651 ISBN 978-1-4244-7542-1 Medium  
  Area Expedition Conference ICPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ GoP2010 Serial 1480  
Permanent link to this record
 
 
Author (up) Albert Gordo; Florent Perronnin edit  doi
isbn  openurl
  Title Asymmetric Distances for Binary Embeddings Type Conference Article
  Year 2011 Publication IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 729 - 736  
  Keywords  
  Abstract In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH) and Semi-Supervised Hashing (SSH). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. We also propose a novel simple binary embedding technique – PCA Embedding (PCAE) – which is shown to yield competitive results with respect to more complex algorithms such as SH and SSH.  
  Address Providence, RI  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-1-4577-0394-2 Medium  
  Area Expedition Conference CVPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ GoP2011; IAM @ iam @ GoP2011 Serial 1817  
Permanent link to this record
 
 
Author (up) Albert Gordo; Florent Perronnin; Ernest Valveny edit   pdf
url  doi
openurl 
  Title Large-scale document image retrieval and classification with runlength histograms and binary embeddings Type Journal Article
  Year 2013 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 46 Issue 7 Pages 1898-1905  
  Keywords visual document descriptor; compression; large-scale; retrieval; classification  
  Abstract We present a new document image descriptor based on multi-scale runlength
histograms. This descriptor does not rely on layout analysis and can be
computed efficiently. We show how this descriptor can achieve state-of-theart
results on two very different public datasets in classification and retrieval
tasks. Moreover, we show how we can compress and binarize these descriptors
to make them suitable for large-scale applications. We can achieve state-ofthe-
art results in classification using binary descriptors of as few as 16 to 64
bits.		  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  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; 600.042; 600.045; 605.203 Approved no  
  Call Number Admin @ si @ GPV2013 Serial 2306  
Permanent link to this record
 
 
Author (up) 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  
  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 (up) Albert Gordo; Florent Perronnin; Yunchao Gong; Svetlana Lazebnik edit   pdf
doi  openurl
  Title Asymmetric Distances for Binary Embeddings Type Journal Article
  Year 2014 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 36 Issue 1 Pages 33-47  
  Keywords  
  Abstract In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH), PCA Embedding (PCAE), PCA Embedding with random rotations (PCAE-RR), and PCA Embedding with iterative quantization (PCAE-ITQ). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0162-8828 ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.045; 605.203; 600.077 Approved no  
  Call Number Admin @ si @ GPG2014 Serial 2272  
Permanent link to this record
 
 
Author (up) Albert Gordo; Jaume Gibert; Ernest Valveny; Marçal Rusiñol edit  doi
isbn  openurl
  Title A Kernel-based Approach to Document Retrieval Type Conference Article
  Year 2010 Publication 9th IAPR International Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages 377–384  
  Keywords  
  Abstract In this paper we tackle the problem of document image retrieval by combining a similarity measure between documents and the probability that a given document belongs to a certain class. The membership probability to a specific class is computed using Support Vector Machines in conjunction with similarity measure based kernel applied to structural document representations. In the presented experiments, we use different document representations, both visual and structural, and we apply them to a database of historical documents. We show how our method based on similarity kernels outperforms the usual distance-based retrieval.  
  Address Boston; USA;  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  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 @ GGV2010 Serial 1431  
Permanent link to this record
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