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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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Miquel Ferrer, Ernest Valveny and F. Serratosa. 2009. Median Graph Computation by means of a Genetic Approach Based on Minimum Common Supergraph and Maximum Common Subraph. 4th Iberian Conference on Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 346–353. (LNCS.)
Abstract: 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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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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Miquel Ferrer, Dimosthenis Karatzas, Ernest Valveny and Horst Bunke. 2009. A Recursive Embedding Approach to Median Graph Computation. 7th IAPR – TC–15 Workshop on Graph–Based Representations in Pattern Recognition. Springer Berlin Heidelberg, 113–123. (LNCS.)
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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Liu Wenyin, Josep Llados and Jean-Marc Ogier. 2008. Graphics Recognition. Recent Advances and New Opportunities.. (LNCS.)
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Sergio Escalera, Alicia Fornes, Oriol Pujol, Josep Llados and Petia Radeva. 2007. Multi-class Binary Object Categorization using Blurred Shape Models. Progress in Pattern Recognition, Image Analysis and Applications, 12th Iberoamerican Congress on Pattern.773–782. (LCNS.)
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Agnes Borras and Josep Llados. 2007. Similarity-Based Object Retrieval Using Appearance and Geometric Feature Combination. 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:113–120.33–39.
Abstract: This work presents a content-based image retrieval system of general purpose that deals with cluttered scenes containing a given query object. The system is flexible enough to handle with a single image of an object despite its rotation, translation and scale variations. The image content is divided in parts that are described with a combination of features based on geometrical and color properties. The idea behind the feature combination is to benefit from a fuzzy similarity computation that provides robustness and tolerance to the retrieval process. The features can be independently computed and the image parts can be easily indexed by using a table structure on every feature value. Finally a process inspired in the alignment strategies is used to check the coherence of the object parts found in a scene. Our work presents a system of easy implementation that uses an open set of features and can suit a wide variety of applications.
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Veronica Romero, Alicia Fornes, Enrique Vidal and Joan Andreu Sanchez. 2017. Information Extraction in Handwritten Marriage Licenses Books Using the MGGI Methodology. In L.A. Alexandre, J.Salvador Sanchez and Joao M. F. Rodriguez, eds. 8th Iberian Conference on Pattern Recognition and Image Analysis.287–294. (LNCS.)
Abstract: Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demographic and genealogical research. For example, marriage license books have been used for centuries by ecclesiastical and secular institutions to register marriages. These books follow a simple structure of the text in the records with a evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. In previous works we studied the use of category-based language models and how a Grammatical Inference technique known as MGGI could improve the accuracy of these tasks. In this work we analyze the main causes of the semantic errors observed in previous results and apply a better implementation of the MGGI technique to solve these problems. Using the resulting language model, transcription and information extraction experiments have been carried out, and the results support our proposed approach.
Keywords: Handwritten Text Recognition; Information extraction; Language modeling; MGGI; Categories-based language model
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Pau Riba, Alicia Fornes and Josep Llados. 2015. Towards the Alignment of Handwritten Music Scores. In Bart Lamiroy and Rafael Dueire Lins, eds. 11th IAPR International Workshop on Graphics Recognition. Springer International Publishing. (LNCS.)
Abstract: It is very common to find different versions of the same music work in archives of Opera Theaters. These differences correspond to modifications and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study. This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such differences. Given the difficulties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the staff lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results.
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Pau Riba, Alicia Fornes and Josep Llados. 2017. Towards the Alignment of Handwritten Music Scores. In Bart Lamiroy and R Dueire Lins, eds. International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges.103–116. (LNCS.)
Abstract: It is very common to nd di erent versions of the same music work in archives of Opera Theaters. These di erences correspond to modi cations and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study.
This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such di erences. Given the diculties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the sta lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results.
Keywords: Optical Music Recognition; Handwritten Music Scores; Dynamic Time Warping alignment
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