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Miquel Ferrer, Ernest Valveny and F. Serratosa. 2009. Median Graphs: A Genetic Approach based on New Theoretical Properties. PR, 42(9), 2003–2012.
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 two major contributions. On one side, and from a theoretical point of view, we show new theoretical properties of the median graph. On the other side, using these new properties, we present a new approximate algorithm based on the genetic search, that improves the computation of the median graph. Finally, we perform 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. With these results, we show how the concept of the median graph can be used in real applications and leaves the box of the only-theoretical concepts, demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs.
Keywords: Median graph; Genetic search; Maximum common subgraph; Graph matching; Structural pattern recognition
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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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Jaume Gibert, Ernest Valveny and Horst Bunke. 2012. Feature Selection on Node Statistics Based Embedding of Graphs. PRL, 33(15), 1980–1990.
Abstract: Representing a graph with a feature vector is a common way of making statistical machine learning algorithms applicable to the domain of graphs. Such a transition from graphs to vectors is known as graphembedding. A key issue in graphembedding is to select a proper set of features in order to make the vectorial representation of graphs as strong and discriminative as possible. In this article, we propose features that are constructed out of frequencies of node label representatives. We first build a large set of features and then select the most discriminative ones according to different ranking criteria and feature transformation algorithms. On different classification tasks, we experimentally show that only a small significant subset of these features is needed to achieve the same classification rates as competing to state-of-the-art methods.
Keywords: Structural pattern recognition; Graph embedding; Feature ranking; PCA; Graph classification
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Partha Pratim Roy, Umapada Pal, Josep Llados and Mathieu Nicolas Delalandre. 2012. Multi-oriented touching text character segmentation in graphical documents using dynamic programming. PR, 45(5), 1972–1983.
Abstract: 2,292 JCR
The touching character segmentation problem becomes complex when touching strings are multi-oriented. Moreover in graphical documents sometimes characters in a single-touching string have different orientations. Segmentation of such complex touching is more challenging. In this paper, we present a scheme towards the segmentation of English multi-oriented touching strings into individual characters. When two or more characters touch, they generate a big cavity region in the background portion. Based on the convex hull information, at first, we use this background information to find some initial points for segmentation of a touching string into possible primitives (a primitive consists of a single character or part of a character). Next, the primitives are merged to get optimum segmentation. A dynamic programming algorithm is applied for this purpose using the total likelihood of characters as the objective function. A SVM classifier is used to find the likelihood of a character. To consider multi-oriented touching strings the features used in the SVM are invariant to character orientation. Experiments were performed in different databases of real and synthetic touching characters and the results show that the method is efficient in segmenting touching characters of arbitrary orientations and sizes.
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Anjan Dutta, Umapada Pal, Alicia Fornes and Josep Llados. 2010. An Efficient Staff Removal Technique from Printed Musical Documents. 20th International Conference on Pattern Recognition.1965–1968.
Abstract: Staff removal is an important preprocessing step of the Optical Music Recognition (OMR). The process aims to remove the stafflines from a musical document and retain only the musical symbols, later these symbols are used effectively to identify the music information. This paper proposes a simple but robust method to remove stafflines from printed musical scores. In the proposed methodology we have considered a staffline segment as a horizontal linkage of vertical black runs with uniform height. We have used the neighbouring properties of a staffline segment to validate it as a true segment. We have considered the dataset along with the deformations described in for evaluation purpose. From experimentation we have got encouraging results.
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Alicia Fornes, Sergio Escalera, Josep Llados and Ernest Valveny. 2010. Symbol Classification using Dynamic Aligned Shape Descriptor. 20th International Conference on Pattern Recognition.1957–1960.
Abstract: Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we propose a new descriptor and distance computation for coping with the problem of symbol recognition in the domain of Graphical Document Image Analysis. The proposed D-Shape descriptor encodes the arrangement information of object parts in a circular structure, allowing different levels of distortion. The classification is performed using a cyclic Dynamic Time Warping based method, allowing distortions and rotation. The methodology has been validated on different data sets, showing very high recognition rates.
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Ilke Demir, Dena Bazazian, Adriana Romero, Viktoriia Sharmanska and Lyne P. Tchapmi. 2018. WiCV 2018: The Fourth Women In Computer Vision Workshop. 4th Women in Computer Vision Workshop.1941–19412.
Abstract: We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations.
Keywords: Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning
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Albert Gordo and Florent Perronnin. 2010. A Bag-of-Pages Approach to Unordered Multi-Page Document Classification. 20th International Conference on Pattern Recognition.1920–1923.
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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Albert Gordo, Florent Perronnin and Ernest Valveny. 2013. Large-scale document image retrieval and classification with runlength histograms and binary embeddings. PR, 46(7), 1898–1905.
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.
Keywords: visual document descriptor; compression; large-scale; retrieval; classification
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Dena Bazazian, Dimosthenis Karatzas and Andrew Bagdanov. 2018. Word Spotting in Scene Images based on Character Recognition. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.1872–1874.
Abstract: In this paper we address the problem of unconstrained Word Spotting in scene images. We train a Fully Convolutional Network to produce heatmaps of all the character classes. Then, we employ the Text Proposals approach and, via a rectangle classifier, detect the most likely rectangle for each query word based on the character attribute maps. We evaluate the proposed method on ICDAR2015 and show that it is capable of identifying and recognizing query words in natural scene images.
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