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Author |
Miguel Angel Bautista; Oriol Pujol; Xavier Baro; Sergio Escalera |
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Title |
Introducing the Separability Matrix for Error Correcting Output Codes Coding |
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Conference Article |
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Year |
2011 |
Publication |
10th International conference on Multiple Classifier Systems |
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Volume |
6713 |
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Pages |
227-236 |
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Abstract |
Error Correcting Output Codes (ECOC) have demonstrate to be a powerful tool for treating multi-class problems. Nevertheless, predefined ECOC designs may not benefit from Error-correcting principles for particular multi-class data. In this paper, we introduce the Separability matrix as a tool to study and enhance designs for ECOC coding. In addition, a novel problem-dependent coding design based on the Separability matrix is tested over a wide set of challenging multi-class problems, obtaining very satisfactory results. |
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Napoles, Italy |
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Springer-Verlag Berlin Heidelberg |
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Carlo Sansone; Josef Kittler; Fabio Roli |
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978-3-642-21556-8 |
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MCS |
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MILAB; OR;HuPBA;MV |
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no |
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Admin @ si @ BPB2011a |
Serial |
1771 |
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Permanent link to this record |
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Author |
Miguel Angel Bautista; Oriol Pujol; Xavier Baro; Sergio Escalera |
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Title |
Introducing the Separability Matrix for Error Correcting Output Codes Coding |
Type |
Conference Article |
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Year |
2011 |
Publication |
10th International Conference on Multiple Classifier Systems |
Abbreviated Journal |
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Volume |
6713 |
Issue |
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Pages |
227-236 |
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Abstract |
Error Correcting Output Codes (ECOC) have demonstrate to be a powerful tool for treating multi-class problems. Nevertheless, predefined ECOC designs may not benefit from Error-correcting principles for particular multi-class data. In this paper, we introduce the Separability matrix as a tool to study and enhance designs for ECOC coding. In addition, a novel problem-dependent coding design based on the Separability matrix is tested over a wide set of challenging multi-class problems, obtaining very satisfactory results. |
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Napoles, Italy |
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Springer-Verlag Berlin, Heidelberg |
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Carlo Sansone; Josef Kittler; Fabio Roli |
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0302-9743 |
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978-3-642-21556-8 |
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MILAB; OR;HuPBA;MV |
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no |
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Call Number |
Admin @ si @ BPB2011b |
Serial |
1887 |
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Author |
Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Petia Radeva |
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Title |
VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering |
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Conference Article |
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Year |
2017 |
Publication |
8th Iberian Conference on Pattern Recognition and Image Analysis |
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Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks |
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In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed. |
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Faro; Portugal; June 2017 |
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IbPRIA |
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MILAB; no proj |
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no |
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Admin @ si @ BPC2017 |
Serial |
2939 |
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Author |
Asma Bensalah; Antonio Parziale; Giuseppe De Gregorio; Angelo Marcelli; Alicia Fornes; Josep Llados |
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Title |
I Can’t Believe It’s Not Better: In-air Movement for Alzheimer Handwriting Synthetic Generation |
Type |
Conference Article |
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Year |
2023 |
Publication |
21st International Graphonomics Conference |
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136–148 |
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During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based models. To alleviate this problem, some works resort to synthetic data generation. Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models. In this work, we combine the domain knowledge about the Alzheimer’s disease for handwriting and use it for a more guided data generation. Concretely, we have explored the use of in-air movements for synthetic data generation. |
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Evora; Portugal; October 2023 |
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IGS |
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DAG |
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no |
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Admin @ si @ BPG2023 |
Serial |
3838 |
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Author |
Q. Bao; Marçal Rusiñol; M.Coustaty; Muhammad Muzzamil Luqman; C.D. Tran; Jean-Marc Ogier |
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Title |
Delaunay triangulation-based features for Camera-based document image retrieval system |
Type |
Conference Article |
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Year |
2016 |
Publication |
12th IAPR Workshop on Document Analysis Systems |
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Pages |
1-6 |
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Keywords |
Camera-based Document Image Retrieval; Delaunay Triangulation; Feature descriptors; Indexing |
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Abstract |
In this paper, we propose a new feature vector, named DElaunay TRIangulation-based Features (DETRIF), for real-time camera-based document image retrieval. DETRIF is computed based on the geometrical constraints from each pair of adjacency triangles in delaunay triangulation which is constructed from centroids of connected components. Besides, we employ a hashing-based indexing system in order to evaluate the performance of DETRIF and to compare it with other systems such as LLAH and SRIF. The experimentation is carried out on two datasets comprising of 400 heterogeneous-content complex linguistic map images (huge size, 9800 X 11768 pixels resolution)and 700 textual document images. |
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Santorini; Greece; April 2016 |
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DAS |
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Notes |
DAG; 600.061; 600.084; 600.077 |
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no |
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Call Number |
Admin @ si @ BRC2016 |
Serial |
2757 |
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Author |
Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
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Title |
Optical Music Recognition by Recurrent Neural Networks |
Type |
Conference Article |
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Year |
2017 |
Publication |
14th IAPR International Workshop on Graphics Recognition |
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Pages |
25-26 |
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Keywords |
Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory |
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Abstract |
Optical Music Recognition is the task of transcribing 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 |
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ICDAR |
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Notes |
DAG; 600.097; 601.302; 600.121 |
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no |
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Call Number |
Admin @ si @ BRC2017 |
Serial |
3056 |
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Author |
Arnau Baro; Pau Riba; Alicia Fornes |
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Title |
Towards the recognition of compound music notes in handwritten music scores |
Type |
Conference Article |
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Year |
2016 |
Publication |
15th international conference on Frontiers in Handwriting Recognition |
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The recognition of handwritten music scores still remains an open problem. The existing approaches can only deal with very simple handwritten scores mainly because of the variability in the handwriting style and the variability in the composition of groups of music notes (i.e. compound music notes). In this work we focus on this second problem and propose a method based on perceptual grouping for the recognition of compound music notes. Our method has been tested using several handwritten music scores of the CVC-MUSCIMA database and compared with a commercial Optical Music Recognition (OMR) software. Given that our method is learning-free, the obtained results are promising. |
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Shenzhen; China; October 2016 |
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2167-6445 |
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ICFHR |
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Notes |
DAG; 600.097 |
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no |
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Admin @ si @ BRF2016 |
Serial |
2903 |
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Author |
Arnau Baro; Pau Riba; Alicia Fornes |
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Title |
A Starting Point for Handwritten Music Recognition |
Type |
Conference Article |
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Year |
2018 |
Publication |
1st International Workshop on Reading Music Systems |
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5-6 |
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Keywords |
Optical Music Recognition; Long Short-Term Memory; Convolutional Neural Networks; MUSCIMA++; CVCMUSCIMA |
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Abstract |
In the last years, the interest in Optical Music Recognition (OMR) has reawakened, especially since the appearance of deep learning. However, there are very few works addressing handwritten scores. In this work we describe a full OMR pipeline for handwritten music scores by using Convolutional and Recurrent Neural Networks that could serve as a baseline for the research community. |
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Paris; France; September 2018 |
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WORMS |
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Notes |
DAG; 600.097; 601.302; 601.330; 600.121 |
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no |
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Admin @ si @ BRF2018 |
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3223 |
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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 |
Type |
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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no |
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Admin @ si @ BRF2019 |
Serial |
3354 |
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Author |
Arnau Baro; Pau Riba; Alicia Fornes |
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Title |
Musigraph: Optical Music Recognition Through Object Detection and Graph Neural Network |
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Conference Article |
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Year |
2022 |
Publication |
Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) |
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Volume |
13639 |
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171-184 |
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Keywords |
Object detection; Optical music recognition; Graph neural network |
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Abstract |
During the last decades, the performance of optical music recognition has been increasingly improving. However, and despite the 2-dimensional nature of music notation (e.g. notes have rhythm and pitch), most works treat musical scores as a sequence of symbols in one dimension, which make their recognition still a challenge. Thus, in this work we explore the use of graph neural networks for musical score recognition. First, because graphs are suited for n-dimensional representations, and second, because the combination of graphs with deep learning has shown a great performance in similar applications. Our methodology consists of: First, we will detect each isolated/atomic symbols (those that can not be decomposed in more graphical primitives) and the primitives that form a musical symbol. Then, we will build the graph taking as root node the notehead and as leaves those primitives or symbols that modify the note’s rhythm (stem, beam, flag) or pitch (flat, sharp, natural). Finally, the graph is translated into a human-readable character sequence for a final transcription and evaluation. Our method has been tested on more than five thousand measures, showing promising results. |
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December 04 – 07, 2022; Hyderabad, India |
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ICFHR |
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DAG; 600.162; 600.140; 602.230 |
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Admin @ si @ BRF2022b |
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3740 |
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Author |
R. Bertrand; Oriol Ramos Terrades; P. Gomez-Kramer; P. Franco; Jean-Marc Ogier |
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Title |
A Conditional Random Field model for font forgery detection |
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Conference Article |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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576 - 580 |
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Nowadays, document forgery is becoming a real issue. A large amount of documents that contain critical information as payment slips, invoices or contracts, are constantly subject to fraudster manipulation because of the lack of security regarding this kind of document. Previously, a system to detect fraudulent documents based on its intrinsic features has been presented. It was especially designed to retrieve copy-move forgery and imperfection due to fraudster manipulation. However, when a set of characters is not present in the original document, copy-move forgery is not feasible. Hence, the fraudster will use a text toolbox to add or modify information in the document by imitating the font or he will cut and paste characters from another document where the font properties are similar. This often results in font type errors. Thus, a clue to detect document forgery consists of finding characters, words or sentences in a document with font properties different from their surroundings. To this end, we present in this paper an automatic forgery detection method based on document font features. Using the Conditional Random Field a measurement of probability that a character belongs to a specific font is made by comparing the character font features to a knowledge database. Then, the character is classified as a genuine or a fake one by comparing its probability to belong to a certain font type with those of the neighboring characters. |
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Nancy; France; August 2015 |
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DAG; 600.077 |
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Admin @ si @ BRG2015 |
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2725 |
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Author |
Oualid M. Benkarim; Petia Radeva; Laura Igual |
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Title |
Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation |
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Conference Article |
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2014 |
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8th Conference on Articulated Motion and Deformable Objects |
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8563 |
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138-147 |
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MRI segmentation; sparse representation; discriminative dic- tionary learning; multiclass classication |
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The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images (MRI) still remains a challenging task. In this paper, we address this problem using sparse representation and discriminative dictionary learning, which have shown promising results in compression, image denoising and recently in MRI segmentation. Particularly, we use multiclass dictionaries learned from a set of brain atlases to simultaneously segment multiple subcortical structures.
We also impose dictionary atoms to be specialized in one given class using label consistent K-SVD, which can alleviate the bias produced by unbalanced libraries, present when dealing with small structures. The proposed method is compared with other state of the art approaches for the segmentation of the Basal Ganglia of 35 subjects of a public dataset.
The promising results of the segmentation method show the eciency of the multiclass discriminative dictionary learning algorithms in MRI segmentation problems. |
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Palma de Mallorca; July 2014 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-08848-8 |
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AMDO |
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MILAB; OR |
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no |
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Admin @ si @ BRI2014 |
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2494 |
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Author |
Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero |
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Title |
Banknote counterfeit detection through background texture printing analysis |
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Conference Article |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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This paper is focused on the detection of counterfeit photocopy banknotes. The main difficulty is to work on a real industrial scenario without any constraint about the acquisition device and with a single image. The main contributions of this paper are twofold: first the adaptation and performance evaluation of existing approaches to classify the genuine and photocopy banknotes using background texture printing analysis, which have not been applied into this context before. Second, a new dataset of Euro banknotes images acquired with several cameras under different luminance conditions to evaluate these methods. Experiments on the proposed algorithms show that mixing SIFT features and sparse coding dictionaries achieves quasi perfect classification using a linear SVM with the created dataset. Approaches using dictionaries to cover all possible texture variations have demonstrated to be robust and outperform the state-of-the-art methods using the proposed benchmark. |
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Rumania; May 2016 |
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DAG; 600.061; 601.269; 600.097 |
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Admin @ si @ BRL2016 |
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2950 |
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Author |
Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero |
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Title |
Evaluation of Texture Descriptors for Validation of Counterfeit Documents |
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Conference Article |
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2017 |
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14th International Conference on Document Analysis and Recognition |
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1237-1242 |
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This paper describes an exhaustive comparative analysis and evaluation of different existing texture descriptor algorithms to differentiate between genuine and counterfeit documents. We include in our experiments different categories of algorithms and compare them in different scenarios with several counterfeit datasets, comprising banknotes and identity documents. Computational time in the extraction of each descriptor is important because the final objective is to use it in a real industrial scenario. HoG and CNN based descriptors stands out statistically over the rest in terms of the F1-score/time ratio performance. |
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2379-2140 |
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DAG; 600.061; 601.269; 600.097; 600.121 |
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Admin @ si @ BRL2017 |
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3092 |
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Author |
Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero |
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Title |
e-Counterfeit: a mobile-server platform for document counterfeit detection |
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Conference Article |
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2017 |
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14th IAPR International Conference on Document Analysis and Recognition |
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This paper presents a novel application to detect counterfeit identity documents forged by a scan-printing operation. Texture analysis approaches are proposed to extract validation features from security background that is usually printed in documents as IDs or banknotes. The main contribution of this work is the end-to-end mobile-server architecture, which provides a service for non-expert users and therefore can be used in several scenarios. The system also provides a crowdsourcing mode so labeled images can be gathered, generating databases for incremental training of the algorithms. |
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Kyoto; Japan; November 2017 |
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DAG; 600.061; 600.097; 600.121 |
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Admin @ si @ BRL2018 |
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3084 |
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