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Mohamed Ali Souibgui; Y.Kessentini |
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Title |
DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement |
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2022 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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44 |
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3 |
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1180-1191 |
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Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems. |
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1 March 2022 |
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DAG; 602.230; 600.121; 600.140 |
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Admin @ si @ SoK2022 |
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3454 |
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Author |
Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados; Thierry Brouard |
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Title |
Fuzzy Multilevel Graph Embedding |
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Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
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PR |
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46 |
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2 |
Pages |
551-565 |
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Pattern recognition; Graphics recognition; Graph clustering; Graph classification; Explicit graph embedding; Fuzzy logic |
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Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition. We extract the topological, structural and attribute information from a graph and encode numeric details by fuzzy histograms and symbolic details by crisp histograms. The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector. Experimentation on standard public graph datasets shows that our method outperforms the state-of-the-art methods of graph embedding for richly attributed graphs. |
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Elsevier |
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0031-3203 |
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DAG; 600.042; 600.045; 605.203 |
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Admin @ si @ LRL2013a |
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2270 |
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Author |
Oriol Ramos Terrades; Albert Berenguel; Debora Gil |
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Title |
A Flexible Outlier Detector Based on a Topology Given by Graph Communities |
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Journal Article |
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2022 |
Publication |
Big Data Research |
Abbreviated Journal |
BDR |
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29 |
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100332 |
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Classification algorithms; Detection algorithms; Description of feature space local structure; Graph communities; Machine learning algorithms; Outlier detectors |
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Outlier detection is essential for optimal performance of machine learning methods and statistical predictive models. Their detection is especially determinant in small sample size unbalanced problems, since in such settings outliers become highly influential and significantly bias models. This particular experimental settings are usual in medical applications, like diagnosis of rare pathologies, outcome of experimental personalized treatments or pandemic emergencies. In contrast to population-based methods, neighborhood based local approaches compute an outlier score from the neighbors of each sample, are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. A main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters, like the number of neighbors.
This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world and synthetic data sets show that our approach outperforms, both, local and global strategies in multi and single view settings. |
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August 28, 2022 |
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DAG; IAM; 600.140; 600.121; 600.139; 600.145; 600.159 |
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Admin @ si @ RBG2022a |
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3718 |
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Author |
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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Year |
2006 |
Publication |
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 @ dag @ RaV2006a |
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635 |
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Author |
Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone |
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Title |
Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework |
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2009 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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31 |
Issue |
9 |
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1630–1644 |
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The combination of the output of classifiers has been one of the strategies used to improve classification rates in general purpose classification systems. Some of the most common approaches can be explained using the Bayes' formula. In this paper, we tackle the problem of the combination of classifiers using a non-Bayesian probabilistic framework. This approach permits us to derive two linear combination rules that minimize misclassification rates under some constraints on the distribution of classifiers. In order to show the validity of this approach we have compared it with other popular combination rules from a theoretical viewpoint using a synthetic data set, and experimentally using two standard databases: the MNIST handwritten digit database and the GREC symbol database. Results on the synthetic data set show the validity of the theoretical approach. Indeed, results on real data show that the proposed methods outperform other common combination schemes. |
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0162-8828 |
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DAG @ dag @ RVT2009 |
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1220 |
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