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Author |
Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel |
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Title |
Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition |
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2017 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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9657 |
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Springer |
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B. Lamiroy; R Dueire Lins |
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GREC |
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DAG; 600.097; 600.121 |
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Call Number |
Admin @ si @ JLR2017 |
Serial |
2928 |
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Author |
Hana Jarraya; Oriol Ramos Terrades; Josep Llados |
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Title |
Learning structural loss parameters on graph embedding applied on symbolic graphs |
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Conference Article |
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Year |
2017 |
Publication |
12th IAPR International Workshop on Graphics Recognition |
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We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset. |
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Kyoto; Japan; November 2017 |
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GREC |
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DAG; 600.097; 600.121 |
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Admin @ si @ JRL2017b |
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3073 |
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Author |
Sounak Dey; Anjan Dutta; Josep Llados; Alicia Fornes; Umapada Pal |
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Title |
Shallow Neural Network Model for Hand-drawn Symbol Recognition in Multi-Writer Scenario |
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Conference Article |
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Year |
2017 |
Publication |
12th IAPR International Workshop on Graphics Recognition |
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31-32 |
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One of the main challenges in hand drawn symbol recognition is the variability among symbols because of the different writer styles. In this paper, we present and discuss some results recognizing hand-drawn symbols with a shallow neural network. A neural network model inspired from the LeNet architecture has been used to achieve state-of-the-art results with
very less training data, which is very unlikely to the data hungry deep neural network. From the results, it has become evident that the neural network architectures can efficiently describe and recognize hand drawn symbols from different writers and can model the inter author aberration |
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GREC |
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DAG; 600.097; 600.121 |
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Admin @ si @ DDL2017 |
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3057 |
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Author |
Pau Riba; Anjan Dutta; Josep Llados; Alicia Fornes |
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Title |
Graph-based deep learning for graphics classification |
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Conference Article |
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Year |
2017 |
Publication |
12th IAPR International Workshop on Graphics Recognition |
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29-30 |
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Graph-based representations are a common way to deal with graphics recognition problems. However, previous works were mainly focused on developing learning-free techniques. The success of deep learning frameworks have proved that learning is a powerful tool to solve many problems, however it is not straightforward to extend these methodologies to non euclidean data such as graphs. On the other hand, graphs are a good representational structure for graphical entities. In this work, we present some deep learning techniques that have been proposed in the literature for graph-based representations and
we show how they can be used in graphics recognition problems |
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GREC |
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DAG; 600.097; 601.302; 600.121 |
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Admin @ si @ RDL2017b |
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3058 |
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Author |
Adria Rico; Alicia Fornes |
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Title |
Camera-based Optical Music Recognition using a Convolutional Neural Network |
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Conference Article |
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Year |
2017 |
Publication |
12th IAPR International Workshop on Graphics Recognition |
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Pages |
27-28 |
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Keywords |
optical music recognition; document analysis; convolutional neural network; deep learning |
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Optical Music Recognition (OMR) consists in recognizing images of music scores. Contrary to expectation, the current OMR systems usually fail when recognizing images of scores captured by digital cameras and smartphones. In this work, we propose a camera-based OMR system based on Convolutional Neural Networks, showing promising preliminary results |
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GREC |
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Notes |
DAG;600.097; 600.121 |
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Call Number |
Admin @ si @ RiF2017 |
Serial |
3059 |
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Author |
Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
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Title |
Optical Music Recognition by Long Short-Term Memory Networks |
Type |
Book Chapter |
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Year |
2018 |
Publication |
Graphics Recognition. Current Trends and Evolutions |
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Volume |
11009 |
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Pages |
81-95 |
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Keywords |
Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory |
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Abstract |
Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach. |
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Springer |
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A. Fornes, B. Lamiroy |
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978-3-030-02283-9 |
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GREC |
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DAG; 600.097; 601.302; 601.330; 600.121 |
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Call Number |
Admin @ si @ BRC2018 |
Serial |
3227 |
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Author |
Asma Bensalah; Pau Riba; Alicia Fornes; Josep Llados |
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Title |
Shoot less and Sketch more: An Efficient Sketch Classification via Joining Graph Neural Networks and Few-shot Learning |
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Conference Article |
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Year |
2019 |
Publication |
13th IAPR International Workshop on Graphics Recognition |
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80-85 |
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Sketch classification; Convolutional Neural Network; Graph Neural Network; Few-shot learning |
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Abstract |
With the emergence of the touchpad devices and drawing tablets, a new era of sketching started afresh. However, the recognition of sketches is still a tough task due to the variability of the drawing styles. Moreover, in some application scenarios there is few labelled data available for training,
which imposes a limitation for deep learning architectures. In addition, in many cases there is a need to generate models able to adapt to new classes. In order to cope with these limitations, we propose a method based on few-shot learning and graph neural networks for classifying sketches aiming for an efficient neural model. We test our approach with several databases of
sketches, showing promising results. |
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Sydney; Australia; September 2019 |
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GREC |
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Notes |
DAG; 600.140; 601.302; 600.121 |
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Admin @ si @ BRF2019 |
Serial |
3354 |
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Author |
Pau Torras; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes |
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Title |
A Transcription Is All You Need: Learning to Align through Attention |
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Conference Article |
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Year |
2021 |
Publication |
14th IAPR International Workshop on Graphics Recognition |
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12916 |
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141–146 |
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Historical ciphered manuscripts are a type of document where graphical symbols are used to encrypt their content instead of regular text. Nowadays, expert transcriptions can be found in libraries alongside the corresponding manuscript images. However, those transcriptions are not aligned, so these are barely usable for training deep learning-based recognition methods. To solve this issue, we propose a method to align each symbol in the transcript of an image with its visual representation by using an attention-based Sequence to Sequence (Seq2Seq) model. The core idea is that, by learning to recognise symbols sequence within a cipher line image, the model also identifies their position implicitly through an attention mechanism. Thus, the resulting symbol segmentation can be later used for training algorithms. The experimental evaluation shows that this method is promising, especially taking into account the small size of the cipher dataset. |
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Virtual; September 2021 |
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GREC |
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DAG; 602.230; 600.140; 600.121 |
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Call Number |
Admin @ si @ TSC2021 |
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3619 |
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Author |
Lei Kang; Juan Ignacio Toledo; Pau Riba; Mauricio Villegas; Alicia Fornes; Marçal Rusiñol |
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Title |
Convolve, Attend and Spell: An Attention-based Sequence-to-Sequence Model for Handwritten Word Recognition |
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Conference Article |
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Year |
2018 |
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40th German Conference on Pattern Recognition |
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459-472 |
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This paper proposes Convolve, Attend and Spell, an attention based sequence-to-sequence model for handwritten word recognition. The proposed architecture has three main parts: an encoder, consisting of a CNN and a bi-directional GRU, an attention mechanism devoted to focus on the pertinent features and a decoder formed by a one-directional GRU, able to spell the corresponding word, character by character. Compared with the recent state-of-the-art, our model achieves competitive results on the IAM dataset without needing any pre-processing step, predefined lexicon nor language model. Code and additional results are available in https://github.com/omni-us/research-seq2seq-HTR. |
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Stuttgart; Germany; October 2018 |
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GCPR |
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DAG; 600.097; 603.057; 302.065; 601.302; 600.084; 600.121; 600.129 |
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Call Number |
Admin @ si @ KTR2018 |
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3167 |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Dimensionality Reduction for Graph of Words Embedding |
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Conference Article |
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Year |
2011 |
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8th IAPR-TC-15 International Workshop. Graph-Based Representations in Pattern Recognition |
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6658 |
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22-31 |
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The Graph of Words Embedding consists in mapping every graph of a given dataset to a feature vector by counting unary and binary relations between node attributes of the graph. While it shows good properties in classification problems, it suffers from high dimensionality and sparsity. These two issues are addressed in this article. Two well-known techniques for dimensionality reduction, kernel principal component analysis (kPCA) and independent component analysis (ICA), are applied to the embedded graphs. We discuss their performance compared to the classification of the original vectors on three different public databases of graphs. |
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Münster, Germany |
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Xiaoyi Jiang; Miquel Ferrer; Andrea Torsello |
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978-3-642-20843-0 |
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GbRPR |
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DAG |
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Admin @ si @ GVB2011a |
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1743 |
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