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M. Visani, Oriol Ramos Terrades and Salvatore Tabbone. 2011. A Protocol to Characterize the Descriptive Power and the Complementarity of Shape Descriptors. IJDAR, 14(1), 87–100.
Abstract: Most document analysis applications rely on the extraction of shape descriptors, which may be grouped into different categories, each category having its own advantages and drawbacks (O.R. Terrades et al. in Proceedings of ICDAR’07, pp. 227–231, 2007). In order to improve the richness of their description, many authors choose to combine multiple descriptors. Yet, most of the authors who propose a new descriptor content themselves with comparing its performance to the performance of a set of single state-of-the-art descriptors in a specific applicative context (e.g. symbol recognition, symbol spotting...). This results in a proliferation of the shape descriptors proposed in the literature. In this article, we propose an innovative protocol, the originality of which is to be as independent of the final application as possible and which relies on new quantitative and qualitative measures. We introduce two types of measures: while the measures of the first type are intended to characterize the descriptive power (in terms of uniqueness, distinctiveness and robustness towards noise) of a descriptor, the second type of measures characterizes the complementarity between multiple descriptors. Characterizing upstream the complementarity of shape descriptors is an alternative to the usual approach where the descriptors to be combined are selected by trial and error, considering the performance characteristics of the overall system. To illustrate the contribution of this protocol, we performed experimental studies using a set of descriptors and a set of symbols which are widely used by the community namely ART and SC descriptors and the GREC 2003 database.
Keywords: Document analysis; Shape descriptors; Symbol description; Performance characterization; Complementarity analysis
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Robert Benavente, Ernest Valveny, Jaume Garcia, Agata Lapedriza, Miquel Ferrer and Gemma Sanchez. 2008. Una experiencia de adaptacion al EEES de las asignaturas de programacion en Ingenieria Informatica.
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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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Sergio Escalera, Alicia Fornes, O. Pujol, Petia Radeva, Gemma Sanchez and Josep Llados. 2009. Blurred Shape Model for Binary and Grey-level Symbol Recognition. PRL, 30(15), 1424–1433.
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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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, Robert Benavente, Ernest Valveny, J. Garcia, Agata Lapedriza and Gemma Sanchez. 2008. Aprendizaje Cooperativo Aplicado a la Docencia de las Asignaturas de Programacion en Ingenieria Informatica.
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Alicia Fornes, Sergio Escalera, Josep Llados, Gemma Sanchez, Petia Radeva and Oriol Pujol. 2007. Handwritten Symbol Recognition by a Boosted Blurred Shape Model with Error Correction. 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:13–21.
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Alicia Fornes, Sergio Escalera, Josep Llados and Gemma Sanchez. 2007. Symbol Recognition by Multi-class Blurred Shape Models. Seventh IAPR International Workshop on Graphics Recognition.11–13.
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Alicia Fornes, Sergio Escalera, Josep Llados, Gemma Sanchez and Joan Mas. 2008. Hand Drawn Symbol Recognition by Blurred Shape Model Descriptor and a Multiclass Classifier. In W. Liu, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities.30–40. (LNCS.)
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