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Author Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel edit  doi
openurl 
  Title Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition Type Book Chapter
  Year 2017 Publication Graphics Recognition. Current Trends and Challenges Abbreviated Journal  
  Volume 9657 Issue Pages  
  Keywords  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor B. Lamiroy; R Dueire Lins  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference GREC  
  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ JLR2017 Serial 2928  
Permanent link to this record
 

 
Author Pau Riba; Alicia Fornes; Josep Llados edit   pdf
url  isbn
openurl 
  Title Towards the Alignment of Handwritten Music Scores Type Book Chapter
  Year 2017 Publication International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges Abbreviated Journal  
  Volume 9657 Issue Pages 103-116  
  Keywords Optical Music Recognition; Handwritten Music Scores; Dynamic Time Warping alignment  
  Abstract It is very common to nd di erent versions of the same music work in archives of Opera Theaters. These di erences correspond to modi cations and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study.
This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such di erences. Given the diculties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the sta lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results.
 
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor Bart Lamiroy; R Dueire Lins  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-319-52158-9 Medium  
  Area Expedition Conference  
  Notes DAG; 600.097; 602.006; 600.121 Approved no  
  Call Number Admin @ si @ RFL2017 Serial 2955  
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Author Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas edit   pdf
url  openurl
  Title Learning to Learn from Web Data through Deep Semantic Embeddings Type Conference Article
  Year 2018 Publication 15th European Conference on Computer Vision Workshops Abbreviated Journal  
  Volume 11134 Issue Pages 514-529  
  Keywords  
  Abstract In this paper we propose to learn a multimodal image and text embedding from Web and Social Media data, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the pipeline can learn from images with associated text without supervision and perform a thourough analysis of five different text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text based image retrieval task, and we clearly outperform state of the art in the MIRFlickr dataset when training in the target data. Further we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings.  
  Address Munich; Alemanya; September 2018  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ECCVW  
  Notes DAG; 600.129; 601.338; 600.121 Approved no  
  Call Number Admin @ si @ GGG2018a Serial 3175  
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Author Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados edit  url
openurl 
  Title Ontology-Based Understanding of Architectural Drawings Type Book Chapter
  Year 2017 Publication International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges Abbreviated Journal  
  Volume 9657 Issue Pages 75-85  
  Keywords Graphics recognition; Floor plan analysi; Domain ontology  
  Abstract In this paper we present a knowledge base of architectural documents aiming at improving existing methods of floor plan classification and understanding. It consists of an ontological definition of the domain and the inclusion of real instances coming from both, automatically interpreted and manually labeled documents. The knowledge base has proven to be an effective tool to structure our knowledge and to easily maintain and upgrade it. Moreover, it is an appropriate means to automatically check the consistency of relational data and a convenient complement of hard-coded knowledge interpretation systems.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ HRL2017 Serial 3086  
Permanent link to this record
 

 
Author Lluis Gomez; Andres Mafla; Marçal Rusiñol; Dimosthenis Karatzas edit   pdf
url  openurl
  Title Single Shot Scene Text Retrieval Type Conference Article
  Year 2018 Publication 15th European Conference on Computer Vision Abbreviated Journal  
  Volume 11218 Issue Pages 728-744  
  Keywords Image retrieval; Scene text; Word spotting; Convolutional Neural Networks; Region Proposals Networks; PHOC  
  Abstract Textual information found in scene images provides high level semantic information about the image and its context and it can be leveraged for better scene understanding. In this paper we address the problem of scene text retrieval: given a text query, the system must return all images containing the queried text. The novelty of the proposed model consists in the usage of a single shot CNN architecture that predicts at the same time bounding boxes and a compact text representation of the words in them. In this way, the text based image retrieval task can be casted as a simple nearest neighbor search of the query text representation over the outputs of the CNN over the entire image
database. Our experiments demonstrate that the proposed architecture
outperforms previous state-of-the-art while it offers a significant increase
in processing speed.
 
  Address Munich; September 2018  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ECCV  
  Notes DAG; 600.084; 601.338; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ GMR2018 Serial 3143  
Permanent link to this record
 

 
Author Alicia Fornes; Bart Lamiroy edit  url
isbn  openurl
  Title Graphics Recognition, Current Trends and Evolutions Type Book Whole
  Year 2018 Publication Graphics Recognition, Current Trends and Evolutions Abbreviated Journal  
  Volume 11009 Issue Pages  
  Keywords  
  Abstract This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Workshop on Graphics Recognition, GREC 2017, held in Kyoto, Japan, in November 2017.
The 10 revised full papers presented were carefully reviewed and selected from 14 initial submissions. They contain both classical and emerging topics of graphics rcognition, namely analysis and detection of diagrams, search and classification, optical music recognition, interpretation of engineering drawings and maps.
 
  Address  
  Corporate Author Thesis  
  Publisher Springer International Publishing Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-030-02283-9 Medium  
  Area Expedition Conference  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ FoL2018 Serial 3171  
Permanent link to this record
 

 
Author Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas edit   pdf
url  openurl
  Title Learning from# Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods Type Conference Article
  Year 2018 Publication 15th European Conference on Computer Vision Workshops Abbreviated Journal  
  Volume 11134 Issue Pages 530-544  
  Keywords  
  Abstract Massive tourism is becoming a big problem for some cities, such as Barcelona, due to its concentration in some neighborhoods. In this work we gather Instagram data related to Barcelona consisting on images-captions pairs and, using the text as a supervisory signal, we learn relations between images, words and neighborhoods. Our goal is to learn which visual elements appear in photos when people is posting about each neighborhood. We perform a language separate treatment of the data and show that it can be extrapolated to a tourists and locals separate analysis, and that tourism is reflected in Social Media at a neighborhood level. The presented pipeline allows analyzing the differences between the images that tourists and locals associate to the different neighborhoods. The proposed method, which can be extended to other cities or subjects, proves that Instagram data can be used to train multi-modal (image and text) machine learning models that are useful to analyze publications about a city at a neighborhood level. We publish the collected dataset, InstaBarcelona and the code used in the analysis.  
  Address Munich; Alemanya; September 2018  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ECCVW  
  Notes DAG; 600.129; 601.338; 600.121 Approved no  
  Call Number Admin @ si @ GGG2018b Serial 3176  
Permanent link to this record
 

 
Author Anguelos Nicolaou; Sounak Dey; V.Christlein; A.Maier; Dimosthenis Karatzas edit   pdf
url  openurl
  Title Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings Type Conference Article
  Year 2018 Publication International Workshop on Reproducible Research in Pattern Recognition Abbreviated Journal  
  Volume 11455 Issue Pages 71-82  
  Keywords  
  Abstract Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ NDC2018 Serial 3178  
Permanent link to this record
 

 
Author Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados edit  doi
openurl 
  Title Automatic Verification of Properly Signed Multi-page Document Images Type Conference Article
  Year 2015 Publication Proceedings of the Eleventh International Symposium on Visual Computing Abbreviated Journal  
  Volume 9475 Issue Pages 327-336  
  Keywords Document Image; Manual Inspection; Signature Verification; Rejection Criterion; Document Flow  
  Abstract In this paper we present an industrial application for the automatic screening of incoming multi-page documents in a banking workflow aimed at determining whether these documents are properly signed or not. The proposed method is divided in three main steps. First individual pages are classified in order to identify the pages that should contain a signature. In a second step, we segment within those key pages the location where the signatures should appear. The last step checks whether the signatures are present or not. Our method is tested in a real large-scale environment and we report the results when checking two different types of real multi-page contracts, having in total more than 14,500 pages.  
  Address Las Vegas, Nevada, USA; December 2015  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume 9475 Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ISVC  
  Notes DAG; 600.077 Approved no  
  Call Number Admin @ si @ Serial 3189  
Permanent link to this record
 

 
Author Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes edit   pdf
doi  isbn
openurl 
  Title Optical Music Recognition by Long Short-Term Memory Networks Type Book Chapter
  Year 2018 Publication Graphics Recognition. Current Trends and Evolutions Abbreviated Journal  
  Volume 11009 Issue Pages 81-95  
  Keywords Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory  
  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.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor A. Fornes, B. Lamiroy  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-030-02283-9 Medium  
  Area Expedition Conference GREC  
  Notes DAG; 600.097; 601.302; 601.330; 600.121 Approved no  
  Call Number Admin @ si @ BRC2018 Serial 3227  
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