| Records |
| Author |
Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados; Thierry Brouard |
| Title |
Fuzzy Multilevel Graph Embedding |
Type |
Journal Article |
| Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
46 |
Issue |
2 |
Pages |
551-565 |
| Keywords |
Pattern recognition; Graphics recognition; Graph clustering; Graph classification; Explicit graph embedding; Fuzzy logic |
| Abstract |
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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Notes  |
DAG; 600.042; 600.045; 605.203 |
Approved |
no |
| Call Number |
Admin @ si @ LRL2013a |
Serial |
2270 |
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| Author |
Anjan Dutta; Josep Llados; Umapada Pal |
| Title |
A symbol spotting approach in graphical documents by hashing serialized graphs |
Type |
Journal Article |
| Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
46 |
Issue |
3 |
Pages |
752-768 |
| Keywords |
Symbol spotting; Graphics recognition; Graph matching; Graph serialization; Graph factorization; Graph paths; Hashing |
| Abstract |
In this paper we propose a symbol spotting technique in graphical documents. Graphs are used to represent the documents and a (sub)graph matching technique is used to detect the symbols in them. We propose a graph serialization to reduce the usual computational complexity of graph matching. Serialization of graphs is performed by computing acyclic graph paths between each pair of connected nodes. Graph paths are one-dimensional structures of graphs which are less expensive in terms of computation. At the same time they enable robust localization even in the presence of noise and distortion. Indexing in large graph databases involves a computational burden as well. We propose a graph factorization approach to tackle this problem. Factorization is intended to create a unified indexed structure over the database of graphical documents. Once graph paths are extracted, the entire database of graphical documents is indexed in hash tables by locality sensitive hashing (LSH) of shape descriptors of the paths. The hashing data structure aims to execute an approximate k-NN search in a sub-linear time. We have performed detailed experiments with various datasets of line drawings and compared our method with the state-of-the-art works. The results demonstrate the effectiveness and efficiency of our technique. |
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Elsevier |
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0031-3203 |
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| Notes |
DAG; 600.042; 600.045; 605.203; 601.152 |
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no |
| Call Number |
Admin @ si @ DLP2012 |
Serial |
2127 |
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| Author |
Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny |
| Title |
Segmentation-free Word Spotting with Exemplar SVMs |
Type |
Journal Article |
| Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
47 |
Issue |
12 |
Pages |
3967–3978 |
| Keywords |
Word spotting; Segmentation-free; Unsupervised learning; Reranking; Query expansion; Compression |
| Abstract |
In this paper we propose an unsupervised segmentation-free method for word spotting in document images. Documents are represented with a grid of HOG descriptors, and a sliding-window approach is used to locate the document regions that are most similar to the query. We use the Exemplar SVM framework to produce a better representation of the query in an unsupervised way. Then, we use a more discriminative representation based on Fisher Vector to rerank the best regions retrieved, and the most promising ones are used to expand the Exemplar SVM training set and improve the query representation. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage. |
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| Notes |
DAG; 600.045; 600.056; 600.061; 602.006; 600.077 |
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no |
| Call Number |
Admin @ si @ AGF2014b |
Serial |
2485 |
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| Author |
Marçal Rusiñol; Josep Llados |
| Title |
Boosting the Handwritten Word Spotting Experience by Including the User in the Loop |
Type |
Journal Article |
| Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
47 |
Issue |
3 |
Pages |
1063–1072 |
| Keywords |
Handwritten word spotting; Query by example; Relevance feedback; Query fusion; Multidimensional scaling |
| Abstract |
In this paper, we study the effect of taking the user into account in a query-by-example handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and two baseline word spotting approaches both based on the bag-of-visual-words model. We finally present two alternative ways of presenting the results to the user that might be more attractive and suitable to the user's needs than the classic ranked list. |
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0031-3203 |
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| Notes |
DAG; 600.045; 600.061; 600.077 |
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no |
| Call Number |
Admin @ si @ RuL2013 |
Serial |
2343 |
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| Author |
Veronica Romero; Alicia Fornes; Nicolas Serrano; Joan Andreu Sanchez; A.H. Toselli; Volkmar Frinken; E. Vidal; Josep Llados |
| Title |
The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition |
Type |
Journal Article |
| Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
46 |
Issue |
6 |
Pages |
1658-1669 |
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| Abstract |
Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demography studies and genealogical research. Automatic processing of historical documents, however, has mostly been focused on single works of literature and less on social records, which tend to have a distinct layout, structure, and vocabulary. Such information is usually collected by expert demographers that devote a lot of time to manually transcribe them. This paper presents a new database, compiled from a marriage license books collection, to support research in automatic handwriting recognition for historical documents containing social records. Marriage license books are documents that were used for centuries by ecclesiastical institutions to register marriage licenses. Books from this collection are handwritten and span nearly half a millennium until the beginning of the 20th century. In addition, a study is presented about the capability of state-of-the-art handwritten text recognition systems, when applied to the presented database. Baseline results are reported for reference in future studies. |
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Elsevier Science Inc. New York, NY, USA |
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0031-3203 |
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| Notes |
DAG; 600.045; 602.006; 605.203 |
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no |
| Call Number |
Admin @ si @ RFS2013 |
Serial |
2298 |
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| Author |
Volkmar Frinken; Andreas Fischer; Markus Baumgartner; Horst Bunke |
| Title |
Keyword spotting for self-training of BLSTM NN based handwriting recognition systems |
Type |
Journal Article |
| Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
47 |
Issue |
3 |
Pages |
1073-1082 |
| Keywords |
Document retrieval; Keyword spotting; Handwriting recognition; Neural networks; Semi-supervised learning |
| Abstract |
The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need for labeling training data, a tedious and costly process. Given a weak initial recognizer trained on labeled data, self-training can be used to recognize unlabeled data and add words that were recognized with high confidence to the training set for re-training. This process is not trivial and requires great care as far as selecting the elements that are to be added to the training set is concerned. In this paper, we propose to use a bidirectional long short-term memory neural network handwritten recognition system for keyword spotting in order to select new elements. A set of experiments shows the high potential of self-training for bootstrapping handwriting recognition systems, both for modern and historical handwritings, and demonstrate the benefits of using keyword spotting over previously published self-training schemes. |
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| Notes |
DAG; 600.077; 602.101 |
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no |
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Admin @ si @ FFB2014 |
Serial |
2297 |
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| Author |
Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas |
| Title |
Improving patch‐based scene text script identification with ensembles of conjoined networks |
Type |
Journal Article |
| Year |
2017 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
67 |
Issue |
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Pages |
85-96 |
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| Notes |
DAG; 600.084; 600.121; 600.129 |
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no |
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Admin @ si @ GNK2017 |
Serial |
2887 |
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| Author |
Lluis Gomez; Dimosthenis Karatzas |
| Title |
TextProposals: a Text‐specific Selective Search Algorithm for Word Spotting in the Wild |
Type |
Journal Article |
| Year |
2017 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
70 |
Issue |
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Pages |
60-74 |
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Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (Goel et al., 2013; Almazán et al., 2014; Jaderberg et al., 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way.
Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (Almazán et al., 2014; Jaderberg et al., 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10% F-score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals. |
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DAG; 600.084; 601.197; 600.121; 600.129 |
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no |
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Admin @ si @ GoK2017 |
Serial |
2886 |
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| Author |
Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu |
| Title |
Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video |
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Journal Article |
| Year |
2018 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
80 |
Issue |
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Pages |
64-82 |
| Keywords |
Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition |
| Abstract |
Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different classes, namely, Animation, Outlet, Sports, e-Learning, Medical, Weather, Defense, Economics, Animal Planet and Technology, for the performance improvement of text detection and recognition, which is an effective approach for scene image or video understanding. For this purpose, at first, we present a new combination of rough and fuzzy concept to study irregular shapes of edge components in input scene videos, which helps to classify edge components into several groups. Next, the proposed method explores gradient direction information of each pixel in each edge component group to extract stroke based features by dividing each group into several intra and inter planes. We further extract correlation and covariance features to encode semantic features located inside planes or between planes. Features of intra and inter planes of groups are then concatenated to get a feature matrix. Finally, the feature matrix is verified with temporal frames and fed to a neural network for categorization. Experimental results show that the proposed method outperforms the existing state-of-the-art methods, at the same time, the performances of text detection and recognition methods are also improved significantly due to categorization. |
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DAG; 600.097; 600.121 |
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no |
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Admin @ si @ RSJ2018 |
Serial |
3096 |
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| Author |
Juan Ignacio Toledo; Manuel Carbonell; Alicia Fornes; Josep Llados |
| Title |
Information Extraction from Historical Handwritten Document Images with a Context-aware Neural Model |
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Journal Article |
| Year |
2019 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
86 |
Issue |
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Pages |
27-36 |
| Keywords |
Document image analysis; Handwritten documents; Named entity recognition; Deep neural networks |
| Abstract |
Many historical manuscripts that hold trustworthy memories of the past societies contain information organized in a structured layout (e.g. census, birth or marriage records). The precious information stored in these documents cannot be effectively used nor accessed without costly annotation efforts. The transcription driven by the semantic categories of words is crucial for the subsequent access. In this paper we describe an approach to extract information from structured historical handwritten text images and build a knowledge representation for the extraction of meaning out of historical data. The method extracts information, such as named entities, without the need of an intermediate transcription step, thanks to the incorporation of context information through language models. Our system has two variants, the first one is based on bigrams, whereas the second one is based on recurrent neural networks. Concretely, our second architecture integrates a Convolutional Neural Network to model visual information from word images together with a Bidirecitonal Long Short Term Memory network to model the relation among the words. This integrated sequential approach is able to extract more information than just the semantic category (e.g. a semantic category can be associated to a person in a record). Our system is generic, it deals with out-of-vocabulary words by design, and it can be applied to structured handwritten texts from different domains. The method has been validated with the ICDAR IEHHR competition protocol, outperforming the existing approaches. |
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DAG; 600.097; 601.311; 603.057; 600.084; 600.140; 600.121 |
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no |
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Admin @ si @ TCF2019 |
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3166 |
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| Author |
Andres Mafla; Ruben Tito; Sounak Dey; Lluis Gomez; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas |
| Title |
Real-time Lexicon-free Scene Text Retrieval |
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Journal Article |
| Year |
2021 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
110 |
Issue |
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Pages |
107656 |
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In this work, we address the task of scene text retrieval: given a text query, the system returns all images containing the queried text. The proposed model uses a single shot CNN architecture that predicts bounding boxes and builds a compact representation of spotted words. In this way, this problem can be modeled as a nearest neighbor search of the textual representation of a query over the outputs of the CNN collected from the totality of an image database. Our experiments demonstrate that the proposed model outperforms previous state-of-the-art, while offering a significant increase in processing speed and unmatched expressiveness with samples never seen at training time. Several experiments to assess the generalization capability of the model are conducted in a multilingual dataset, as well as an application of real-time text spotting in videos. |
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DAG; 600.121; 600.129; 601.338 |
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no |
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Admin @ si @ MTD2021 |
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3493 |
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| Author |
Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas |
| Title |
Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition |
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Journal Article |
| Year |
2022 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
129 |
Issue |
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108766 |
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The advent of recurrent neural networks for handwriting recognition marked an important milestone reaching impressive recognition accuracies despite the great variability that we observe across different writing styles. Sequential architectures are a perfect fit to model text lines, not only because of the inherent temporal aspect of text, but also to learn probability distributions over sequences of characters and words. However, using such recurrent paradigms comes at a cost at training stage, since their sequential pipelines prevent parallelization. In this work, we introduce a non-recurrent approach to recognize handwritten text by the use of transformer models. We propose a novel method that bypasses any recurrence. By using multi-head self-attention layers both at the visual and textual stages, we are able to tackle character recognition as well as to learn language-related dependencies of the character sequences to be decoded. Our model is unconstrained to any predefined vocabulary, being able to recognize out-of-vocabulary words, i.e. words that do not appear in the training vocabulary. We significantly advance over prior art and demonstrate that satisfactory recognition accuracies are yielded even in few-shot learning scenarios. |
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Sept. 2022 |
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DAG; 600.121; 600.162 |
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no |
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Admin @ si @ KRR2022 |
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3556 |
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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 |
Publication |
Pattern Recognition |
Abbreviated Journal |
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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no |
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Admin @ si @ JSK2022 |
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3613 |
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Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes |
| Title |
Learning graph edit distance by graph neural networks |
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Journal Article |
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2021 |
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Pattern Recognition |
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PR |
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120 |
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108132 |
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The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words i.e. keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset. |
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DAG; 600.140; 600.121 |
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Admin @ si @ RFL2021 |
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3611 |
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| Author |
Souhail Bakkali; Zuheng Ming; Mickael Coustaty; Marçal Rusiñol; Oriol Ramos Terrades |
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VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification |
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Journal Article |
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2023 |
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Pattern Recognition |
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PR |
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139 |
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109419 |
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Multimodal learning from document data has achieved great success lately as it allows to pre-train semantically meaningful features as a prior into a learnable downstream approach. In this paper, we approach the document classification problem by learning cross-modal representations through language and vision cues, considering intra- and inter-modality relationships. Instead of merging features from different modalities into a common representation space, the proposed method exploits high-level interactions and learns relevant semantic information from effective attention flows within and across modalities. The proposed learning objective is devised between intra- and inter-modality alignment tasks, where the similarity distribution per task is computed by contracting positive sample pairs while simultaneously contrasting negative ones in the common feature representation space}. Extensive experiments on public document classification datasets demonstrate the effectiveness and the generalization capacity of our model on both low-scale and large-scale datasets. |
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ISSN 0031-3203 |
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DAG; 600.140; 600.121 |
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Admin @ si @ BMC2023 |
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3826 |
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