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Albert Gordo and Ernest Valveny. 2009. The diagonal split: A pre-segmentation step for page layout analysis & classification. 4th Iberian Conference on Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 290–297. (LNCS.)
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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Marçal Rusiñol and Josep Llados. 2009. Logo Spotting by a Bag-of-words Approach for Document Categorization. 10th International Conference on Document Analysis and Recognition.111–115.
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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Sergio Escalera, Alicia Fornes, Oriol Pujol, Alberto Escudero and Petia Radeva. 2009. Circular Blurred Shape Model for Symbol Spotting in Documents. 16th IEEE International Conference on Image Processing.1985–1988.
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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Sergio Escalera, Alicia Fornes, Oriol Pujol and Petia Radeva. 2009. Multi-class Binary Symbol Classification with Circular Blurred Shape Models. 15th International Conference on Image Analysis and Processing. Springer Berlin Heidelberg, 1005–1014. (LNCS.)
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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Miquel Ferrer, Ernest Valveny, F. Serratosa, I. Bardaji and Horst Bunke. 2009. Graph-based k-means clustering: A comparison of the set versus the generalized median graph. 13th International Conference on Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 342–350. (LNCS.)
Abstract: In this paper we propose the application of the generalized median graph in a graph-based k-means clustering algorithm. In the graph-based k-means algorithm, the centers of the clusters have been traditionally represented using the set median graph. We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters. Experiments on three databases show that using the generalized median graph as the clusters representative yields better results than the set median graph.
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Ricard Coll, Alicia Fornes and Josep Llados. 2009. Graphological Analysis of Handwritten Text Documents for Human Resources Recruitment. 10th International Conference on Document Analysis and Recognition.1081–1085.
Abstract: The use of graphology in recruitment processes has become a popular tool in many human resources companies. This paper presents a model that links features from handwritten images to a number of personality characteristics used to measure applicant aptitudes for the job in a particular hiring scenario. In particular we propose a model of measuring active personality and leadership of the writer. Graphological features that define such a profile are measured in terms of document and script attributes like layout configuration, letter size, shape, slant and skew angle of lines, etc. After the extraction, data is classified using a neural network. An experimental framework with real samples has been constructed to illustrate the performance of the approach.
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Alicia Fornes, Josep Llados, Gemma Sanchez and Horst Bunke. 2009. Symbol-independent writer identification in old handwritten music scores. In proceedings of 8th IAPR International Workshop on Graphics Recognition. Springer Berlin Heidelberg, 186–197.
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Alicia Fornes, Josep Llados, Gemma Sanchez and Horst Bunke. 2009. On the use of textural features for writer identification in old handwritten music scores. 10th International Conference on Document Analysis and Recognition.996–1000.
Abstract: Writer identification consists in determining the writer of a piece of handwriting from a set of writers. In this paper we present a system for writer identification in old handwritten music scores which uses only music notation to determine the author. The steps of the proposed system are the following. First of all, the music sheet is preprocessed for obtaining a music score without the staff lines. Afterwards, four different methods for generating texture images from music symbols are applied. Every approach uses a different spatial variation when combining the music symbols to generate the textures. Finally, Gabor filters and Grey-scale Co-ocurrence matrices are used to obtain the features. The classification is performed using a k-NN classifier based on Euclidean distance. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving encouraging identification rates.
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Agnes Borras and Josep Llados. 2009. Corest: A measure of color and space stability to detect salient regions according to human criteria. 5th International Conference on Computer Vision Theory and Applications.204–209.
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Salim Jouili, Salvatore Tabbone and Ernest Valveny. 2009. Evaluation of graph matching measures for documents retrieval. In proceedings of 8th IAPR International Workshop on Graphics Recognition.13–21.
Abstract: In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used which include line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each grahp distance measure depends on the kind of data and the graph representation technique.
Keywords: Graph Matching; Graph retrieval; structural representation; Performance Evaluation
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