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Author Jaume Gibert; Ernest Valveny; Horst Bunke
Title (up) Feature Selection on Node Statistics Based Embedding of Graphs Type Journal Article
Year 2012 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 33 Issue 15 Pages 1980–1990
Keywords Structural pattern recognition; Graph embedding; Feature ranking; PCA; Graph classification
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.
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Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes DAG Approved no
Call Number Admin @ si @ GVB2012b Serial 1993
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Author Mohamed Ali Souibgui; Alicia Fornes; Yousri Kessentini; Beata Megyesi
Title (up) Few shots are all you need: A progressive learning approach for low resource handwritten text recognition Type Journal Article
Year 2022 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 160 Issue Pages 43-49
Keywords
Abstract Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in the following repository: https://github.com/dali92002/HTRbyMatching
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Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.121; 600.162; 602.230 Approved no
Call Number Admin @ si @ SFK2022 Serial 3736
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Author Josep Llados; Horst Bunke; Enric Marti
Title (up) Finding rotational symmetries by cyclic string matching Type Journal Article
Year 1997 Publication Pattern recognition letters Abbreviated Journal PRL
Volume 18 Issue 14 Pages 1435-1442
Keywords Rotational symmetry; Reflectional symmetry; String matching
Abstract 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
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG;IAM; Approved no
Call Number IAM @ iam @ LBM1997a Serial 1562
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Author Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes
Title (up) From Optical Music Recognition to Handwritten Music Recognition: a Baseline Type Journal Article
Year 2019 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 123 Issue Pages 1-8
Keywords
Abstract Optical Music Recognition (OMR) is the branch of document image analysis that aims to convert images of musical scores into a computer-readable format. Despite decades of research, the recognition of handwritten music scores, concretely the Western notation, is still an open problem, and the few existing works only focus on a specific stage of OMR. In this work, we propose a full Handwritten Music Recognition (HMR) system based on Convolutional Recurrent Neural Networks, data augmentation and transfer learning, that can serve as a baseline for the research community.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.097; 601.302; 601.330; 600.140; 600.121 Approved no
Call Number Admin @ si @ BRC2019 Serial 3275
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Author Pau Riba; Josep Llados; Alicia Fornes
Title (up) Hierarchical graphs for coarse-to-fine error tolerant matching Type Journal Article
Year 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 134 Issue Pages 116-124
Keywords Hierarchical graph representation; Coarse-to-fine graph matching; Graph-based retrieval
Abstract During the last years, graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their ability to capture both structural and appearance-based information. Thus, they provide a greater representational power than classical statistical frameworks. However, graph-based representations leads to high computational complexities usually dealt by graph embeddings or approximated matching techniques. Despite their representational power, they are very sensitive to noise and small variations of the input image. With the aim to cope with the time complexity and the variability present in the generated graphs, in this paper we propose to construct a novel hierarchical graph representation. Graph clustering techniques adapted from social media analysis have been used in order to contract a graph at different abstraction levels while keeping information about the topology. Abstract nodes attributes summarise information about the contracted graph partition. For the proposed representations, a coarse-to-fine matching technique is defined. Hence, small graphs are used as a filtering before more accurate matching methods are applied. This approach has been validated in real scenarios such as classification of colour images or retrieval of handwritten words (i.e. word spotting).
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Notes DAG; 600.097; 601.302; 603.057; 600.140; 600.121 Approved no
Call Number Admin @ si @ RLF2020 Serial 3349
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Author Debora Gil; Petia Radeva
Title (up) Inhibition of false landmarks Type Journal Article
Year 2006 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 27 Issue 9 Pages 1022-1030
Keywords
Abstract Corners and junctions are landmarks characterized by the lack of differentiability in the unit tangent to the image level curve. Detectors based on differential operators are not, by their own definition, the best posed as they require a higher degree of differentiability to yield a reliable response. We argue that a corner detector should be based on the degree of continuity of the tangent vector to the image level sets, work on the image domain and need no assumptions on neither the image local structure nor the particular geometry of the corner/junction. An operator measuring the degree of differentiability of the projection matrix on the image gradient fulfills the above requirements. Because using smoothing kernels leads to corner misplacement, we suggest an alternative fake response remover based on the receptive field inhibition of spurious details. The combination of both orientation discontinuity detection and noise inhibition produce our inhibition orientation energy (IOE) landmark locator.
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Corporate Author Thesis
Publisher Elsevier Science Inc. Place of Publication New York, NY, USA Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0167-8655 ISBN Medium
Area Expedition Conference
Notes IAM;MILAB Approved no
Call Number IAM @ iam @ GiR2006 Serial 1529
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Author Fadi Dornaika; Angel Sappa
Title (up) Instantaneous 3D motion from image derivatives using the Least Trimmed Square Regression Type Journal Article
Year 2009 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 30 Issue 5 Pages 535–543
Keywords
Abstract This paper presents a new technique to the instantaneous 3D motion estimation. The main contributions are as follows. First, we show that the 3D camera or scene velocity can be retrieved from image derivatives only assuming that the scene contains a dominant plane. Second, we propose a new robust algorithm that simultaneously provides the Least Trimmed Square solution and the percentage of inliers-the non-contaminated data. Experiments on both synthetic and real image sequences demonstrated the effectiveness of the developed method. Those experiments show that the new robust approach can outperform classical robust schemes.
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Corporate Author Thesis
Publisher Elsevier Science Inc. Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0167-8655 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ DoS2009a Serial 1115
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Author David Guillamet; Jordi Vitria; B. Shiele
Title (up) Introducing a weighted non-negative matrix factorization for image classification Type Journal Article
Year 2003 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 24 Issue 14 Pages 2447–2454
Keywords
Abstract IF: 0.809
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Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ GVS2003 Serial 382
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Author Frederic Sampedro; Sergio Escalera; Anna Puig
Title (up) Iterative Multiclass Multiscale Stacked Sequential Learning: definition and application to medical volume segmentation Type Journal Article
Year 2014 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 46 Issue Pages 1-10
Keywords Machine learning; Sequential learning; Multi-class problems; Contextual learning; Medical volume segmentation
Abstract In this work we present the iterative multi-class multi-scale stacked sequential learning framework (IMMSSL), a novel learning scheme that is particularly suited for medical volume segmentation applications. This model exploits the inherent voxel contextual information of the structures of interest in order to improve its segmentation performance results. Without any feature set or learning algorithm prior assumption, the proposed scheme directly seeks to learn the contextual properties of a region from the predicted classifications of previous classifiers within an iterative scheme. Performance results regarding segmentation accuracy in three two-class and multi-class medical volume datasets show a significant improvement with respect to state of the art alternatives. Due to its easiness of implementation and its independence of feature space and learning algorithm, the presented machine learning framework could be taken into consideration as a first choice in complex volume segmentation scenarios.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ SEP2014 Serial 2550
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Author B. Gautam; Oriol Ramos Terrades; Joana Maria Pujadas-Mora; Miquel Valls-Figols
Title (up) Knowledge graph based methods for record linkage Type Journal Article
Year 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 136 Issue Pages 127-133
Keywords
Abstract Nowadays, it is common in Historical Demography the use of individual-level data as a consequence of a predominant life-course approach for the understanding of the demographic behaviour, family transition, mobility, etc. Advanced record linkage is key since it allows increasing the data complexity and its volume to be analyzed. However, current methods are constrained to link data from the same kind of sources. Knowledge graph are flexible semantic representations, which allow to encode data variability and semantic relations in a structured manner.

In this paper we propose the use of knowledge graph methods to tackle record linkage tasks. The proposed method, named WERL, takes advantage of the main knowledge graph properties and learns embedding vectors to encode census information. These embeddings are properly weighted to maximize the record linkage performance. We have evaluated this method on benchmark data sets and we have compared it to related methods with stimulating and satisfactory results.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes DAG; 600.140; 600.121 Approved no
Call Number Admin @ si @ GRP2020 Serial 3453
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Author Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta
Title (up) Large-scale graph indexing using binary embeddings of node contexts for information spotting in document image databases Type Journal Article
Year 2017 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 87 Issue Pages 203-211
Keywords
Abstract 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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Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.097; 602.006; 603.053; 600.121 Approved no
Call Number RLF2017b Serial 2873
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Author A. Martinez; Jordi Vitria
Title (up) Learning mixture models using a genetic version of the EM algorithm. Type Journal Article
Year 2000 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 21 Issue 8 Pages 759–769
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Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ MVi2000 Serial 335
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Author Miquel Ferrer; Ernest Valveny; F. Serratosa
Title (up) Median graph: A new exact algorithm using a distance based on the maximum common subgraph Type Journal Article
Year 2009 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 30 Issue 5 Pages 579–588
Keywords
Abstract 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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Corporate Author Thesis
Publisher Elsevier Science Inc. Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0167-8655 ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number DAG @ dag @ FVS2009a Serial 1114
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Author Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Petia Radeva; Jordi Vitria; Oriol Pujol
Title (up) Minimal Design of Error-Correcting Output Codes Type Journal Article
Year 2011 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 33 Issue 6 Pages 693-702
Keywords Multi-class classification; Error-correcting output codes; Ensemble of classifiers
Abstract IF JCR CCIA 1.303 2009 54/103
The classification of large number of object categories is a challenging trend in the pattern recognition field. In literature, this is often addressed using an ensemble of classifiers. In this scope, the Error-correcting output codes framework has demonstrated to be a powerful tool for combining classifiers. However, most state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration. The results over several public UCI datasets and different multi-class computer vision problems show that the proposed methodology obtains comparable (even better) results than state-of-the-art ECOC methodologies with far less number of dichotomizers.
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Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0167-8655 ISBN Medium
Area Expedition Conference
Notes MILAB; OR;HuPBA;MV Approved no
Call Number Admin @ si @ BEB2011a Serial 1800
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Author Albert Clapes; Miguel Reyes; Sergio Escalera
Title (up) Multi-modal User Identification and Object Recognition Surveillance System Type Journal Article
Year 2013 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 34 Issue 7 Pages 799-808
Keywords Multi-modal RGB-Depth data analysis; User identification; Object recognition; Intelligent surveillance; Visual features; Statistical learning
Abstract We propose an automatic surveillance system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized using robust statistical approaches. The system robustly recognizes users and updates the system in an online way, identifying and detecting new actors in the scene. Moreover, segmented objects are described, matched, recognized, and updated online using view-point 3D descriptions, being robust to partial occlusions and local 3D viewpoint rotations. Finally, the system saves the historic of user–object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; 600.046; 605.203;MILAB Approved no
Call Number Admin @ si @ CRE2013 Serial 2248
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