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Author |
Oriol Pujol; Sergio Escalera; Petia Radeva |
Title |
An Incremental Node Embedding Technique for Error Correcting Output Codes |
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Journal |
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2008 |
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Pattern Recognition |
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PR |
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41 |
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2 |
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713–725 |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ PER2008 |
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942 |
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Author |
Mario Hernandez; Joao Sanchez; Jordi Vitria |
Title |
Selected papers from Iberian Conference on Pattern Recognition and Image Analysis |
Type |
Book Whole |
Year |
2012 |
Publication |
Pattern Recognition |
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45 |
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9 |
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3047-3582 |
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0031-3203 |
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OR;MV |
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no |
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Admin @ si @ HSV2012 |
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2069 |
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Author |
Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas |
Title |
Improving patch‐based scene text script identification with ensembles of conjoined networks |
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Journal Article |
Year |
2017 |
Publication |
Pattern Recognition |
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PR |
Volume |
67 |
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85-96 |
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DAG; 600.084; 600.121; 600.129 |
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no |
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Admin @ si @ GNK2017 |
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2887 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados; Mathieu Nicolas Delalandre |
Title |
Multi-oriented touching text character segmentation in graphical documents using dynamic programming |
Type |
Journal Article |
Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
Volume |
45 |
Issue |
5 |
Pages |
1972-1983 |
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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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0031-3203 |
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DAG |
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no |
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Admin @ si @ RPL2012a |
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2133 |
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Author |
Ivan Huerta; Marco Pedersoli; Jordi Gonzalez; Alberto Sanfeliu |
Title |
Combining where and what in change detection for unsupervised foreground learning in surveillance |
Type |
Journal Article |
Year |
2015 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
Volume |
48 |
Issue |
3 |
Pages |
709-719 |
Keywords |
Object detection; Unsupervised learning; Motion segmentation; Latent variables; Support vector machine; Multiple appearance models; Video surveillance |
Abstract |
Change detection is the most important task for video surveillance analytics such as foreground and anomaly detection. Current foreground detectors learn models from annotated images since the goal is to generate a robust foreground model able to detect changes in all possible scenarios. Unfortunately, manual labelling is very expensive. Most advanced supervised learning techniques based on generic object detection datasets currently exhibit very poor performance when applied to surveillance datasets because of the unconstrained nature of such environments in terms of types and appearances of objects. In this paper, we take advantage of change detection for training multiple foreground detectors in an unsupervised manner. We use statistical learning techniques which exploit the use of latent parameters for selecting the best foreground model parameters for a given scenario. In essence, the main novelty of our proposed approach is to combine the where (motion segmentation) and what (learning procedure) in change detection in an unsupervised way for improving the specificity and generalization power of foreground detectors at the same time. We propose a framework based on latent support vector machines that, given a noisy initialization based on motion cues, learns the correct position, aspect ratio, and appearance of all moving objects in a particular scene. Specificity is achieved by learning the particular change detections of a given scenario, and generalization is guaranteed since our method can be applied to any possible scene and foreground object, as demonstrated in the experimental results outperforming the state-of-the-art. |
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ISE; 600.063; 600.078 |
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no |
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Admin @ si @ HPG2015 |
Serial |
2589 |
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Author |
Estefania Talavera; Carolin Wuerich; Nicolai Petkov; Petia Radeva |
Title |
Topic modelling for routine discovery from egocentric photo-streams |
Type |
Journal Article |
Year |
2020 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
Volume |
104 |
Issue |
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Pages |
107330 |
Keywords |
Routine; Egocentric vision; Lifestyle; Behaviour analysis; Topic modelling |
Abstract |
Developing tools to understand and visualize lifestyle is of high interest when addressing the improvement of habits and well-being of people. Routine, defined as the usual things that a person does daily, helps describe the individuals’ lifestyle. With this paper, we are the first ones to address the development of novel tools for automatic discovery of routine days of an individual from his/her egocentric images. In the proposed model, sequences of images are firstly characterized by semantic labels detected by pre-trained CNNs. Then, these features are organized in temporal-semantic documents to later be embedded into a topic models space. Finally, Dynamic-Time-Warping and Spectral-Clustering methods are used for final day routine/non-routine discrimination. Moreover, we introduce a new EgoRoutine-dataset, a collection of 104 egocentric days with more than 100.000 images recorded by 7 users. Results show that routine can be discovered and behavioural patterns can be observed. |
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MILAB; no proj |
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no |
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Admin @ si @ TWP2020 |
Serial |
3435 |
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Author |
Ruben Tito; Dimosthenis Karatzas; Ernest Valveny |
Title |
Hierarchical multimodal transformers for Multi-Page DocVQA |
Type |
Journal Article |
Year |
2023 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
Volume |
144 |
Issue |
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Pages |
109834 |
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Abstract |
Document Visual Question Answering (DocVQA) refers to the task of answering questions from document images. Existing work on DocVQA only considers single-page documents. However, in real scenarios documents are mostly composed of multiple pages that should be processed altogether. In this work we extend DocVQA to the multi-page scenario. For that, we first create a new dataset, MP-DocVQA, where questions are posed over multi-page documents instead of single pages. Second, we propose a new hierarchical method, Hi-VT5, based on the T5 architecture, that overcomes the limitations of current methods to process long multi-page documents. The proposed method is based on a hierarchical transformer architecture where the encoder summarizes the most relevant information of every page and then, the decoder takes this summarized information to generate the final answer. Through extensive experimentation, we demonstrate that our method is able, in a single stage, to answer the questions and provide the page that contains the relevant information to find the answer, which can be used as a kind of explainability measure. |
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ISSN 0031-3203 |
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DAG; 600.155; 600.121 |
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no |
Call Number |
Admin @ si @ TKV2023 |
Serial |
3825 |
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Author |
Xavier Soria; Angel Sappa; Patricio Humanante; Arash Akbarinia |
Title |
Dense extreme inception network for edge detection |
Type |
Journal Article |
Year |
2023 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
Volume |
139 |
Issue |
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Pages |
109461 |
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Abstract |
Edge detection is the basis of many computer vision applications. State of the art predominantly relies on deep learning with two decisive factors: dataset content and network architecture. Most of the publicly available datasets are not curated for edge detection tasks. Here, we address this limitation. First, we argue that edges, contours and boundaries, despite their overlaps, are three distinct visual features requiring separate benchmark datasets. To this end, we present a new dataset of edges. Second, we propose a novel architecture, termed Dense Extreme Inception Network for Edge Detection (DexiNed), that can be trained from scratch without any pre-trained weights. DexiNed outperforms other algorithms in the presented dataset. It also generalizes well to other datasets without any fine-tuning. The higher quality of DexiNed is also perceptually evident thanks to the sharper and finer edges it outputs. |
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MSIAU |
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no |
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Admin @ si @ SSH2023 |
Serial |
3982 |
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Author |
Ruben Tito; Dimosthenis Karatzas; Ernest Valveny |
Title |
Hierarchical multimodal transformers for Multipage DocVQA |
Type |
Journal Article |
Year |
2023 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
Volume |
144 |
Issue |
109834 |
Pages |
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Abstract |
Existing work on DocVQA only considers single-page documents. However, in real applications documents are mostly composed of multiple pages that should be processed altogether. In this work, we propose a new multimodal hierarchical method Hi-VT5, that overcomes the limitations of current methods to process long multipage documents. In contrast to previous hierarchical methods that focus on different semantic granularity (He et al., 2021) or different subtasks (Zhou et al., 2022) used in image classification. Our method is a hierarchical transformer architecture where the encoder learns to summarize the most relevant information of every page and then, the decoder uses this summarized representation to generate the final answer, following a bottom-up approach. Moreover, due to the lack of multipage DocVQA datasets, we also introduce MP-DocVQA, an extension of SP-DocVQA where questions are posed over multipage documents instead of single pages. Through extensive experimentation, we demonstrate that Hi-VT5 is able, in a single stage, to answer the questions and provide the page that contains the answer, which can be used as a kind of explainability measure. |
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DAG |
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no |
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Admin @ si @ TKV2023 |
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3836 |
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Author |
Pau Rodriguez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez |
Title |
Age and gender recognition in the wild with deep attention |
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Journal Article |
Year |
2017 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
Volume |
72 |
Issue |
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Pages |
563-571 |
Keywords |
Age recognition; Gender recognition; Deep neural networks; Attention mechanisms |
Abstract |
Face analysis in images in the wild still pose a challenge for automatic age and gender recognition tasks, mainly due to their high variability in resolution, deformation, and occlusion. Although the performance has highly increased thanks to Convolutional Neural Networks (CNNs), it is still far from optimal when compared to other image recognition tasks, mainly because of the high sensitiveness of CNNs to facial variations. In this paper, inspired by biology and the recent success of attention mechanisms on visual question answering and fine-grained recognition, we propose a novel feedforward attention mechanism that is able to discover the most informative and reliable parts of a given face for improving age and gender classification. In particular, given a downsampled facial image, the proposed model is trained based on a novel end-to-end learning framework to extract the most discriminative patches from the original high-resolution image. Experimental validation on the standard Adience, Images of Groups, and MORPH II benchmarks show that including attention mechanisms enhances the performance of CNNs in terms of robustness and accuracy. |
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ISE; 600.098; 602.133; 600.119 |
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no |
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Admin @ si @ RCG2017b |
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2962 |
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Author |
Miguel Angel Bautista; Sergio Escalera; Oriol Pujol |
Title |
On the Design of an ECOC-Compliant Genetic Algorithm |
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Journal Article |
Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
Volume |
47 |
Issue |
2 |
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865-884 |
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Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches. |
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HuPBA;MILAB |
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no |
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Admin @ si @ BEP2013 |
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2254 |
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Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa |
Title |
Median Graphs: A Genetic Approach based on New Theoretical Properties |
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Journal Article |
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2009 |
Publication |
Pattern Recognition |
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PR |
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42 |
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9 |
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2003–2012 |
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Median graph; Genetic search; Maximum common subgraph; Graph matching; Structural pattern recognition |
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. |
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DAG |
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no |
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DAG @ dag @ FVS2009b |
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1167 |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
Title |
Graph Embedding in Vector Spaces by Node Attribute Statistics |
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Journal Article |
Year |
2012 |
Publication |
Pattern Recognition |
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PR |
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45 |
Issue |
9 |
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3072-3083 |
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Structural pattern recognition; Graph embedding; Data clustering; Graph classification |
Abstract |
Graph-based representations are of broad use and applicability in pattern recognition. They exhibit, however, a major drawback with regards to the processing tools that are available in their domain. Graphembedding into vectorspaces is a growing field among the structural pattern recognition community which aims at providing a feature vector representation for every graph, and thus enables classical statistical learning machinery to be used on graph-based input patterns. In this work, we propose a novel embedding methodology for graphs with continuous nodeattributes and unattributed edges. The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. We specifically deal with an important issue of this methodology, namely, the selection of a suitable set of representatives. In an experimental evaluation, we empirically show the advantages of this novel approach in the context of different classification problems using several databases of graphs. |
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DAG |
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Admin @ si @ GVB2012a |
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1992 |
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S.K. Jemni; Mohamed Ali Souibgui; Yousri Kessentini; Alicia Fornes |
Title |
Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image Enhancement |
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Journal Article |
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2022 |
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Pattern Recognition |
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PR |
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123 |
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108370 |
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Handwritten document images can be highly affected by degradation for different reasons: Paper ageing, daily-life scenarios (wrinkles, dust, etc.), bad scanning process and so on. These artifacts raise many readability issues for current Handwritten Text Recognition (HTR) algorithms and severely devalue their efficiency. In this paper, we propose an end to end architecture based on Generative Adversarial Networks (GANs) to recover the degraded documents into a and form. Unlike the most well-known document binarization methods, which try to improve the visual quality of the degraded document, the proposed architecture integrates a handwritten text recognizer that promotes the generated document image to be more readable. To the best of our knowledge, this is the first work to use the text information while binarizing handwritten documents. Extensive experiments conducted on degraded Arabic and Latin handwritten documents demonstrate the usefulness of integrating the recognizer within the GAN architecture, which improves both the visual quality and the readability of the degraded document images. Moreover, we outperform the state of the art in H-DIBCO challenges, after fine tuning our pre-trained model with synthetically degraded Latin handwritten images, on this task. |
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DAG; 600.124; 600.121; 602.230 |
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Admin @ si @ JSK2022 |
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3613 |
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Author |
Jose Antonio Rodriguez; Florent Perronnin |
Title |
Handwritten word-spotting using hidden Markov models and universal vocabularies |
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Journal Article |
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2009 |
Publication |
Pattern Recognition |
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PR |
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42 |
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9 |
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2103-2116 |
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Word-spotting; Hidden Markov model; Score normalization; Universal vocabulary; Handwriting recognition |
Abstract |
Handwritten word-spotting is traditionally viewed as an image matching task between one or multiple query word-images and a set of candidate word-images in a database. This is a typical instance of the query-by-example paradigm. In this article, we introduce a statistical framework for the word-spotting problem which employs hidden Markov models (HMMs) to model keywords and a Gaussian mixture model (GMM) for score normalization. We explore the use of two types of HMMs for the word modeling part: continuous HMMs (C-HMMs) and semi-continuous HMMs (SC-HMMs), i.e. HMMs with a shared set of Gaussians. We show on a challenging multi-writer corpus that the proposed statistical framework is always superior to a traditional matching system which uses dynamic time warping (DTW) for word-image distance computation. A very important finding is that the SC-HMM is superior when labeled training data is scarce—as low as one sample per keyword—thanks to the prior information which can be incorporated in the shared set of Gaussians. |
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Elsevier |
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Call Number |
Admin @ si @ RoP2009 |
Serial |
1053 |
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