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Author  |
Albert Gordo |

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Title |
Document Image Representation, Classification and Retrieval in Large-Scale Domains |
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Book Whole |
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Year |
2013 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Abstract |
Despite the “paperless office” ideal that started in the decade of the seventies, businesses still strive against an increasing amount of paper documentation. Companies still receive huge amounts of paper documentation that need to be analyzed and processed, mostly in a manual way. A solution for this task consists in, first, automatically scanning the incoming documents. Then, document images can be analyzed and information can be extracted from the data. Documents can also be automatically dispatched to the appropriate workflows, used to retrieve similar documents in the dataset to transfer information, etc.
Due to the nature of this “digital mailroom”, we need document representation methods to be general, i.e., able to cope with very different types of documents. We need the methods to be sound, i.e., able to cope with unexpected types of documents, noise, etc. And, we need to methods to be scalable, i.e., able to cope with thousands or millions of documents that need to be processed, stored, and consulted. Unfortunately, current techniques of document representation, classification and retrieval are not apt for this digital mailroom framework, since they do not fulfill some or all of these requirements.
Through this thesis we focus on the problem of document representation aimed at classification and retrieval tasks under this digital mailroom framework. We first propose a novel document representation based on runlength histograms, and extend it to cope with more complex documents such as multiple-page documents, or documents that contain more sources of information such as extracted OCR text. Then we focus on the scalability requirements and propose a novel binarization method which we dubbed PCAE, as well as two general asymmetric distances between binary embeddings that can significantly improve the retrieval results at a minimal extra computational cost. Finally, we note the importance of supervised learning when performing large-scale retrieval, and study several approaches that can significantly boost the results at no extra cost at query time. |
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Barcelona |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Ernest Valveny;Florent Perronnin |
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DAG |
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no |
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Call Number |
Admin @ si @ Gor2013 |
Serial |
2277 |
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Permanent link to this record |
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Author  |
Albert Gordo; Alicia Fornes; Ernest Valveny |


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Title |
Writer identification in handwritten musical scores with bags of notes |
Type |
Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
46 |
Issue |
5 |
Pages |
1337-1345 |
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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. |
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0031-3203 |
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DAG |
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no |
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Call Number |
Admin @ si @ GFV2013 |
Serial |
2307 |
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Author  |
Albert Gordo; Alicia Fornes; Ernest Valveny; Josep Llados |


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Title |
A Bag of Notes Approach to Writer Identification in Old Handwritten Music Scores |
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Conference Article |
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Year |
2010 |
Publication |
9th IAPR International Workshop on Document Analysis Systems |
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Pages |
247–254 |
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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. |
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Boston; USA; |
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ISBN |
978-1-60558-773-8 |
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DAS |
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Notes |
DAG |
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no |
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Call Number |
DAG @ dag @ GFV2010 |
Serial |
1320 |
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Author  |
Albert Gordo; Ernest Valveny |


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Title |
A rotation invariant page layout descriptor for document classification and retrieval |
Type |
Conference Article |
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Year |
2009 |
Publication |
10th International Conference on Document Analysis and Recognition |
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Pages |
481–485 |
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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. |
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Address |
Barcelona, Spain |
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Edition |
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ISSN |
1520-5363 |
ISBN |
978-1-4244-4500-4 |
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Conference |
ICDAR |
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Notes |
DAG |
Approved |
no |
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Call Number |
DAG @ dag @ GoV2009a |
Serial |
1175 |
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Permanent link to this record |
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Author  |
Albert Gordo; Ernest Valveny |


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Title |
The diagonal split: A pre-segmentation step for page layout analysis & classification |
Type |
Conference Article |
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Year |
2009 |
Publication |
4th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
5524 |
Issue |
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Pages |
290–297 |
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Keywords |
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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. |
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Address |
Póvoa de Varzim, Portugal |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-02171-8 |
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Conference |
IbPRIA |
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Notes |
DAG |
Approved |
no |
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Call Number |
DAG @ dag @ Gov2009b |
Serial |
1176 |
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Permanent link to this record |
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Author  |
Albert Gordo; Florent Perronnin |


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Title |
A Bag-of-Pages Approach to Unordered Multi-Page Document Classification |
Type |
Conference Article |
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Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
Abbreviated Journal |
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Issue |
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Pages |
1920–1923 |
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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. |
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Address |
Istanbul (Turkey) |
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ISSN |
1051-4651 |
ISBN |
978-1-4244-7542-1 |
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Conference |
ICPR |
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Notes |
DAG |
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no |
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Call Number |
Admin @ si @ GoP2010 |
Serial |
1480 |
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Permanent link to this record |
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Author  |
Albert Gordo; Florent Perronnin |


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Title |
Asymmetric Distances for Binary Embeddings |
Type |
Conference Article |
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Year |
2011 |
Publication |
IEEE Conference on Computer Vision and Pattern Recognition |
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Pages |
729 - 736 |
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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. |
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Providence, RI |
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978-1-4577-0394-2 |
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Conference |
CVPR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ GoP2011; IAM @ iam @ GoP2011 |
Serial |
1817 |
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Permanent link to this record |
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Author  |
Albert Gordo; Florent Perronnin; Ernest Valveny |


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Title |
Large-scale document image retrieval and classification with runlength histograms and binary embeddings |
Type |
Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
46 |
Issue |
7 |
Pages |
1898-1905 |
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Keywords |
visual document descriptor; compression; large-scale; retrieval; classification |
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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. |
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Elsevier |
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0031-3203 |
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Notes |
DAG; 600.042; 600.045; 605.203 |
Approved |
no |
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Call Number |
Admin @ si @ GPV2013 |
Serial |
2306 |
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Permanent link to this record |
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Author  |
Albert Gordo; Florent Perronnin; Ernest Valveny |


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Title |
Document classification using multiple views |
Type |
Conference Article |
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Year |
2012 |
Publication |
10th IAPR International Workshop on Document Analysis Systems |
Abbreviated Journal |
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Issue |
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Pages |
33-37 |
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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. |
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Address |
Australia |
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Publisher |
IEEE Computer Society Washington |
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978-0-7695-4661-2 |
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DAG |
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no |
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Call Number |
Admin @ si @ GPV2012 |
Serial |
2049 |
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Permanent link to this record |
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Author  |
Albert Gordo; Florent Perronnin; Yunchao Gong; Svetlana Lazebnik |


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Title |
Asymmetric Distances for Binary Embeddings |
Type |
Journal Article |
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Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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Volume |
36 |
Issue |
1 |
Pages |
33-47 |
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Keywords |
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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. |
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0162-8828 |
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Notes |
DAG; 600.045; 605.203; 600.077 |
Approved |
no |
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Call Number |
Admin @ si @ GPG2014 |
Serial |
2272 |
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Permanent link to this record |