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Author Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov edit   pdf
openurl 
  Title Soft-PHOC Descriptor for End-to-End Word Spotting in Egocentric Scene Images Type Conference Article
  Year 2018 Publication International Workshop on Egocentric Perception, Interaction and Computing at ECCV Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Word spotting in natural scene images has many applications in scene understanding and visual assistance. We propose Soft-PHOC, an intermediate representation of images based on character probability maps. Our representation extends the concept of the Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We show how to use our descriptors for word spotting tasks in egocentric camera streams through an efficient text line proposal algorithm. This is based on the Hough Transform over character attribute maps followed by scoring using Dynamic Time Warping (DTW). We evaluate our results on ICDAR 2015 Challenge 4 dataset of incidental scene text captured by an egocentric camera.  
  Address Munich; Alemanya; September 2018  
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  Publisher Place of Publication Editor  
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  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference ECCVW  
  Notes DAG; 600.129; 600.121; Approved no  
  Call Number (up) Admin @ si @ BKB2018b Serial 3174  
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Author Souhail Bakkali; Zuheng Ming; Mickael Coustaty; Marçal Rusiñol; Oriol Ramos Terrades edit   pdf
openurl 
  Title VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification Type Miscellaneous
  Year 2022 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract 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.  
  Address  
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  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.140; 600.121 Approved no  
  Call Number (up) Admin @ si @ BMC2022 Serial 3826  
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Author Ali Furkan Biten; Andres Mafla; Lluis Gomez; Dimosthenis Karatzas edit   pdf
url  doi
openurl 
  Title Is An Image Worth Five Sentences? A New Look into Semantics for Image-Text Matching Type Conference Article
  Year 2022 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 1391-1400  
  Keywords Measurement; Training; Integrated circuits; Annotations; Semantics; Training data; Semisupervised learning  
  Abstract The task of image-text matching aims to map representations from different modalities into a common joint visual-textual embedding. However, the most widely used datasets for this task, MSCOCO and Flickr30K, are actually image captioning datasets that offer a very limited set of relationships between images and sentences in their ground-truth annotations. This limited ground truth information forces us to use evaluation metrics based on binary relevance: given a sentence query we consider only one image as relevant. However, many other relevant images or captions may be present in the dataset. In this work, we propose two metrics that evaluate the degree of semantic relevance of retrieved items, independently of their annotated binary relevance. Additionally, we incorporate a novel strategy that uses an image captioning metric, CIDEr, to define a Semantic Adaptive Margin (SAM) to be optimized in a standard triplet loss. By incorporating our formulation to existing models, a large improvement is obtained in scenarios where available training data is limited. We also demonstrate that the performance on the annotated image-caption pairs is maintained while improving on other non-annotated relevant items when employing the full training set. The code for our new metric can be found at github. com/furkanbiten/ncsmetric and the model implementation at github. com/andrespmd/semanticadaptive_margin.  
  Address Virtual; Waikoloa; Hawai; USA; January 2022  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
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  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference WACV  
  Notes DAG; 600.155; 302.105; Approved no  
  Call Number (up) Admin @ si @ BMG2022 Serial 3663  
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Author Carlos Boned Riera; Oriol Ramos Terrades edit  doi
openurl 
  Title Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph Type Conference Article
  Year 2022 Publication 26th International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 2186-2191  
  Keywords Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition  
  Abstract Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks.  
  Address Montreal; Quebec; Canada; August 2022  
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  ISSN ISBN Medium  
  Area Expedition Conference ICPR  
  Notes DAG; 600.121; 600.162 Approved no  
  Call Number (up) Admin @ si @ BoR2022 Serial 3741  
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Author Q. Bao; Marçal Rusiñol; M.Coustaty; Muhammad Muzzamil Luqman; C.D. Tran; Jean-Marc Ogier edit   pdf
doi  openurl
  Title Delaunay triangulation-based features for Camera-based document image retrieval system Type Conference Article
  Year 2016 Publication 12th IAPR Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages 1-6  
  Keywords Camera-based Document Image Retrieval; Delaunay Triangulation; Feature descriptors; Indexing  
  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.  
  Address Santorini; Greece; April 2016  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
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  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference DAS  
  Notes DAG; 600.061; 600.084; 600.077 Approved no  
  Call Number (up) Admin @ si @ BRC2016 Serial 2757  
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Author Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes edit   pdf
doi  openurl
  Title Optical Music Recognition by Recurrent Neural Networks Type Conference Article
  Year 2017 Publication 14th IAPR International Workshop on Graphics Recognition Abbreviated Journal  
  Volume Issue Pages 25-26  
  Keywords Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory  
  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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  Corporate Author Thesis  
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  ISSN ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.097; 601.302; 600.121 Approved no  
  Call Number (up) Admin @ si @ BRC2017 Serial 3056  
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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 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 (up) Admin @ si @ BRC2018 Serial 3227  
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Author Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes edit  url
openurl 
  Title From Optical Music Recognition to Handwritten Music Recognition: a Baseline Type Journal Article
  Year 2019 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 123 Issue Pages 1-8  
  Keywords  
  Abstract Optical Music Recognition (OMR) is the branch of document image analysis that aims to convert images of musical scores into a computer-readable format. Despite decades of research, the recognition of handwritten music scores, concretely the Western notation, is still an open problem, and the few existing works only focus on a specific stage of OMR. In this work, we propose a full Handwritten Music Recognition (HMR) system based on Convolutional Recurrent Neural Networks, data augmentation and transfer learning, that can serve as a baseline for the research community.  
  Address  
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  Notes DAG; 600.097; 601.302; 601.330; 600.140; 600.121 Approved no  
  Call Number (up) Admin @ si @ BRC2019 Serial 3275  
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Author Arnau Baro; Pau Riba; Alicia Fornes edit   pdf
doi  openurl
  Title Towards the recognition of compound music notes in handwritten music scores Type Conference Article
  Year 2016 Publication 15th international conference on Frontiers in Handwriting Recognition Abbreviated Journal  
  Volume Issue Pages  
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  Abstract 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.  
  Address Shenzhen; China; October 2016  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2167-6445 ISBN Medium  
  Area Expedition Conference ICFHR  
  Notes DAG; 600.097 Approved no  
  Call Number (up) Admin @ si @ BRF2016 Serial 2903  
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Author Arnau Baro; Pau Riba; Alicia Fornes edit   pdf
openurl 
  Title A Starting Point for Handwritten Music Recognition Type Conference Article
  Year 2018 Publication 1st International Workshop on Reading Music Systems Abbreviated Journal  
  Volume Issue Pages 5-6  
  Keywords Optical Music Recognition; Long Short-Term Memory; Convolutional Neural Networks; MUSCIMA++; CVCMUSCIMA  
  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.  
  Address Paris; France; September 2018  
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  ISSN ISBN Medium  
  Area Expedition Conference WORMS  
  Notes DAG; 600.097; 601.302; 601.330; 600.121 Approved no  
  Call Number (up) Admin @ si @ BRF2018 Serial 3223  
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