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Author |
Gemma Sanchez; Josep Llados; K. Tombre |
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A mean string algorithm to compute the average among a set of 2D shapes |
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2002 |
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Pattern Recognition Letters |
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23 |
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1-3 |
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203–214 |
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DAG; IF: 0.409 |
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DAG @ dag @ SLT2002 |
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275 |
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Oriol Ramos Terrades; Ernest Valveny |
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Title |
A new use of the ridgelets transform for describing linear singularities in images |
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2006 |
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Pattern Recognition Letters |
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PRL |
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27 |
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6 |
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587–596 |
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DAG |
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DAG @ dag @ RaV2006a |
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635 |
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Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa |
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Title |
Median graph: A new exact algorithm using a distance based on the maximum common subgraph |
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Journal Article |
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Year |
2009 |
Publication |
Pattern Recognition Letters |
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PRL |
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Volume |
30 |
Issue |
5 |
Pages |
579–588 |
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Median graphs have been presented as a useful tool for capturing the essential information of a set of graphs. Nevertheless, computation of optimal solutions is a very hard problem. In this work we present a new and more efficient optimal algorithm for the median graph computation. With the use of a particular cost function that permits the definition of the graph edit distance in terms of the maximum common subgraph, and a prediction function in the backtracking algorithm, we reduce the size of the search space, avoiding the evaluation of a great amount of states and still obtaining the exact median. We present a set of experiments comparing our new algorithm against the previous existing exact algorithm using synthetic data. In addition, we present the first application of the exact median graph computation to real data and we compare the results against an approximate algorithm based on genetic search. These experimental results show that our algorithm outperforms the previous existing exact algorithm and in addition show the potential applicability of the exact solutions to real problems. |
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Elsevier Science Inc. |
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0167-8655 |
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DAG |
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DAG @ dag @ FVS2009a |
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1114 |
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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 |
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Year |
2010 |
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Pattern Recognition Letters |
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PRL |
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31 |
Issue |
3 |
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188–201 |
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Document image analysis and recognition, Graphics recognition, Symbol spotting ,Vectorial representations, Line-drawings |
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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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DAG @ dag @ RBL2010 |
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1177 |
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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 |
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Journal Article |
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Year |
2009 |
Publication |
Pattern Recognition Letters |
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PRL |
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Volume |
30 |
Issue |
15 |
Pages |
1424–1433 |
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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 |
Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados |
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Title |
Unsupervised writer adaptation of whole-word HMMs with application to word-spotting |
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Journal Article |
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Year |
2010 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
31 |
Issue |
8 |
Pages |
742–749 |
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Keywords |
Word-spotting; Handwriting recognition; Writer adaptation; Hidden Markov model; Document analysis |
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In this paper we propose a novel approach for writer adaptation in a handwritten word-spotting task. The method exploits the fact that the semi-continuous hidden Markov model separates the word model parameters into (i) a codebook of shapes and (ii) a set of word-specific parameters.
Our main contribution is to employ this property to derive writer-specific word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document. Experimental results show an increase in performance when this adaptation technique is applied. To the best of our knowledge, this is the first work dealing with adaptation for word-spotting. The preliminary version of this paper obtained an IBM Best Student Paper Award at the 19th International Conference on Pattern Recognition. |
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Elsevier |
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DAG |
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DAG @ dag @ RPS2010 |
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1290 |
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Author |
Josep Llados; Horst Bunke; Enric Marti |
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Title |
Finding rotational symmetries by cyclic string matching |
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Journal Article |
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Year |
1997 |
Publication |
Pattern recognition letters |
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PRL |
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Volume |
18 |
Issue |
14 |
Pages |
1435-1442 |
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Rotational symmetry; Reflectional symmetry; String matching |
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Symmetry is an important shape feature. In this paper, a simple and fast method to detect perfect and distorted rotational symmetries of 2D objects is described. The boundary of a shape is polygonally approximated and represented as a string. Rotational symmetries are found by cyclic string matching between two identical copies of the shape string. The set of minimum cost edit sequences that transform the shape string to a cyclically shifted version of itself define the rotational symmetry and its order. Finally, a modification of the algorithm is proposed to detect reflectional symmetries. Some experimental results are presented to show the reliability of the proposed algorithm |
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Elsevier |
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DAG;IAM; |
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IAM @ iam @ LBM1997a |
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1562 |
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Author |
Ernest Valveny; Enric Marti |
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A model for image generation and symbol recognition through the deformation of lineal shapes |
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2003 |
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Pattern Recognition Letters |
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PRL |
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24 |
Issue |
15 |
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2857-2867 |
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We describe a general framework for the recognition of distorted images of lineal shapes, which relies on three items: a model to represent lineal shapes and their deformations, a model for the generation of distorted binary images and the combination of both models in a common probabilistic framework, where the generation of deformations is related to an internal energy, and the generation of binary images to an external energy. Then, recognition consists in the minimization of a global energy function, performed by using the EM algorithm. This general framework has been applied to the recognition of hand-drawn lineal symbols in graphic documents. |
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Elsevier Science Inc. |
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New York, NY, USA |
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0167-8655 |
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DAG; IAM |
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IAM @ iam @ VAM2003 |
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1653 |
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Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Feature Selection on Node Statistics Based Embedding of Graphs |
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2012 |
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Pattern Recognition Letters |
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33 |
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15 |
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1980–1990 |
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Structural pattern recognition; Graph embedding; Feature ranking; PCA; Graph classification |
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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. |
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Admin @ si @ GVB2012b |
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1993 |
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Author |
Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta |
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Large-scale graph indexing using binary embeddings of node contexts for information spotting in document image databases |
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Journal Article |
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2017 |
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Pattern Recognition Letters |
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87 |
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203-211 |
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Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations. However, retrieving a query graph from a large dataset of graphs implies a high computational complexity. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. With this aim, in this paper we propose a graph indexation formalism applied to visual retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Then, each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in different real scenarios such as handwritten word spotting in images of historical documents or symbol spotting in architectural floor plans. |
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DAG; 600.097; 602.006; 603.053; 600.121 |
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RLF2017b |
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2873 |
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