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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Petia Radeva |
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Title |
Multi-class Binary Symbol Classification with Circular Blurred Shape Models |
Type |
Conference Article |
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Year |
2009 |
Publication |
15th International Conference on Image Analysis and Processing |
Abbreviated Journal |
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Volume |
5716 |
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Pages |
1005–1014 |
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Abstract |
Multi-class binary symbol classification requires the use of rich descriptors and robust classifiers. Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we present the Circular Blurred Shape Model descriptor. This descriptor encodes the arrangement information of object parts in a correlogram structure. A prior blurring degree defines the level of distortion allowed to the symbol. Moreover, we learn the new feature space using a set of Adaboost classifiers, which are combined in the Error-Correcting Output Codes framework to deal with the multi-class categorization problem. The presented work has been validated over different multi-class data sets, and compared to the state-of-the-art descriptors, showing significant performance improvements. |
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Salerno, Italy |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-04145-7 |
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ICIAP |
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MILAB;HuPBA;DAG |
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no |
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Call Number |
BCNPCL @ bcnpcl @ EFP2009c |
Serial |
1186 |
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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Alberto Escudero; Petia Radeva |
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Title |
Circular Blurred Shape Model for Symbol Spotting in Documents |
Type |
Conference Article |
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Year |
2009 |
Publication |
16th IEEE International Conference on Image Processing |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
1985-1988 |
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Abstract |
Symbol spotting problem requires feature extraction strategies able to generalize from training samples and to localize the target object while discarding most part of the image. In the case of document analysis, symbol spotting techniques have to deal with a high variability of symbols' appearance. In this paper, we propose the Circular Blurred Shape Model descriptor. Feature extraction is performed capturing the spatial arrangement of significant object characteristics in a correlogram structure. Shape information from objects is shared among correlogram regions, being tolerant to the irregular deformations. Descriptors are learnt using a cascade of classifiers and Abadoost as the base classifier. Finally, symbol spotting is performed by means of a windowing strategy using the learnt cascade over plan and old musical score documents. Spotting and multi-class categorization results show better performance comparing with the state-of-the-art descriptors. |
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Cairo, Egypt |
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978-1-4244-5653-6 |
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ICIP |
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Notes |
MILAB;HuPBA;DAG |
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no |
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Call Number |
BCNPCL @ bcnpcl @ EFP2009b |
Serial |
1184 |
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Author |
Sergio Escalera; Alicia Fornes; O. Pujol; Petia Radeva; Gemma Sanchez; Josep Llados |
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Title |
Blurred Shape Model for Binary and Grey-level Symbol Recognition |
Type |
Journal Article |
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Year |
2009 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
30 |
Issue |
15 |
Pages |
1424–1433 |
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Abstract |
Many symbol recognition problems require the use of robust descriptors in order to obtain rich information of the data. However, the research of a good descriptor is still an open issue due to the high variability of symbols appearance. Rotation, partial occlusions, elastic deformations, intra-class and inter-class variations, or high variability among symbols due to different writing styles, are just a few problems. In this paper, we introduce a symbol shape description to deal with the changes in appearance that these types of symbols suffer. The shape of the symbol is aligned based on principal components to make the recognition invariant to rotation and reflection. Then, we present the Blurred Shape Model descriptor (BSM), where new features encode the probability of appearance of each pixel that outlines the symbols shape. Moreover, we include the new descriptor in a system to deal with multi-class symbol categorization problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split symbol classes. Then, the binary problems are embedded in an Error-Correcting Output Codes framework (ECOC) to deal with the multi-class case. The methodology is evaluated on different synthetic and real data sets. State-of-the-art descriptors and classifiers are compared, showing the robustness and better performance of the present scheme to classify symbols with high variability of appearance. |
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Notes |
HuPBA; DAG; MILAB |
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no |
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Call Number |
BCNPCL @ bcnpcl @ EFP2009a |
Serial |
1180 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Logo Spotting by a Bag-of-words Approach for Document Categorization |
Type |
Conference Article |
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Year |
2009 |
Publication |
10th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
111–115 |
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Abstract |
In this paper we present a method for document categorization which processes incoming document images such as invoices or receipts. The categorization of these document images is done in terms of the presence of a certain graphical logo detected without segmentation. The graphical logos are described by a set of local features and the categorization of the documents is performed by the use of a bag-of-words model. Spatial coherence rules are added to reinforce the correct category hypothesis, aiming also to spot the logo inside the document image. Experiments which demonstrate the effectiveness of this system on a large set of real data are presented. |
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Barcelona; Spain |
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ISSN |
1520-5363 |
ISBN |
978-1-4244-4500-4 |
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ICDAR |
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Notes |
DAG |
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no |
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Call Number |
DAG @ dag @ RuL2009b |
Serial |
1179 |
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Permanent link to this record |
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Author |
Marçal Rusiñol; Agnes Borras; Josep Llados |
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Title |
Relational Indexing of Vectorial Primitives for Symbol Spotting in Line-Drawing Images |
Type |
Journal Article |
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Year |
2010 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
31 |
Issue |
3 |
Pages |
188–201 |
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Keywords |
Document image analysis and recognition, Graphics recognition, Symbol spotting ,Vectorial representations, Line-drawings |
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Abstract |
This paper presents a symbol spotting approach for indexing by content a database of line-drawing images. As line-drawings are digital-born documents designed by vectorial softwares, instead of using a pixel-based approach, we present a spotting method based on vector primitives. Graphical symbols are represented by a set of vectorial primitives which are described by an off-the-shelf shape descriptor. A relational indexing strategy aims to retrieve symbol locations into the target documents by using a combined numerical-relational description of 2D structures. The zones which are likely to contain the queried symbol are validated by a Hough-like voting scheme. In addition, a performance evaluation framework for symbol spotting in graphical documents is proposed. The presented methodology has been evaluated with a benchmarking set of architectural documents achieving good performance results. |
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Elsevier |
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DAG |
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Call Number |
DAG @ dag @ RBL2010 |
Serial |
1177 |
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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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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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IbPRIA |
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DAG |
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Call Number |
DAG @ dag @ Gov2009b |
Serial |
1176 |
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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 |
Abbreviated Journal |
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Volume |
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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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Barcelona, Spain |
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1520-5363 |
ISBN |
978-1-4244-4500-4 |
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ICDAR |
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DAG |
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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 |
Miquel Ferrer; Ernest Valveny; F. Serratosa |
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Title |
Median Graph Computation by means of a Genetic Approach Based on Minimum Common Supergraph and Maximum Common Subraph |
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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5524 |
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346–353 |
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Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present a new genetic algorithm for the median graph computation. A set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity, show that we obtain good approximations of the median graph. Finally, we use the median graph in a real nearest neighbour classification showing that it leaves the box of the only-theoretical concepts and demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs. |
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Póvoa de Varzim, Portugal |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-02171-8 |
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IbPRIA |
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DAG |
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no |
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Call Number |
DAG @ dag @ FVS2009c |
Serial |
1174 |
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Author |
Miquel Ferrer; Dimosthenis Karatzas; Ernest Valveny; Horst Bunke |
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Title |
A Recursive Embedding Approach to Median Graph Computation |
Type |
Conference Article |
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Year |
2009 |
Publication |
7th IAPR – TC–15 Workshop on Graph–Based Representations in Pattern Recognition |
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Volume |
5534 |
Issue |
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Pages |
113–123 |
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Abstract |
The median graph has been shown to be a good choice to infer a representative of a set of graphs. It has been successfully applied to graph-based classification and clustering. Nevertheless, its computation is extremely complex. Several approaches have been presented up to now based on different strategies. In this paper we present a new approximate recursive algorithm for median graph computation based on graph embedding into vector spaces. Preliminary experiments on three databases show that this new approach is able to obtain better medians than the previous existing approaches. |
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Address |
Venice, Italy |
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Springer Berlin Heidelberg |
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0302-9743 |
ISBN |
978-3-642-02123-7 |
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GBR |
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DAG |
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no |
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Call Number |
DAG @ dag @ FKV2009 |
Serial |
1173 |
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Author |
Antonio Clavelli; Dimosthenis Karatzas |
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Title |
Text Segmentation in Colour Posters from the Spanish Civil War Era |
Type |
Conference Article |
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Year |
2009 |
Publication |
10th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
181 - 185 |
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Abstract |
The extraction of textual content from colour documents of a graphical nature is a complicated task. The text can be rendered in any colour, size and orientation while the existence of complex background graphics with repetitive patterns can make its localization and segmentation extremely difficult.
Here, we propose a new method for extracting textual content from such colour images that makes no assumption as to the size of the characters, their orientation or colour, while it is tolerant to characters that do not follow a straight baseline. We evaluate this method on a collection of documents with historical
connotations: the Posters from the Spanish Civil War. |
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Barcelona, Spain |
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1520-5363 |
ISBN |
978-1-4244-4500-4 |
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ICDAR |
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DAG |
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Call Number |
DAG @ dag @ ClK2009 |
Serial |
1172 |
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