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Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
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Title |
Ontology-Based Understanding of Architectural Drawings |
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Book Chapter |
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Year |
2017 |
Publication |
International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
9657 |
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Pages |
75-85 |
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Keywords |
Graphics recognition; Floor plan analysi; Domain ontology |
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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. |
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DAG; 600.121 |
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no |
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Admin @ si @ HRL2017 |
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3086 |
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Author |
Juan Ignacio Toledo; Sebastian Sudholt; Alicia Fornes; Jordi Cucurull; A. Fink; Josep Llados |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find book details (via ISBN) isbn](http://refbase.cvc.uab.es/img/isbn.gif)
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Title |
Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling |
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Conference Article |
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Year |
2016 |
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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
10029 |
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543-552 |
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Document image analysis; Word image categorization; Convolutional neural networks; Named entity detection |
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Abstract |
The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches. The usual approach combines transcription and grammars in order to extract semantically meaningful entities. In this paper, we describe a new method to obtain word categories directly from non-preprocessed handwritten word images. The method can be used to directly extract information, being an alternative to the transcription. Thus it can be used as a first step in any kind of syntactical analysis. The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images. We performed the experiments on a historical marriage record dataset, obtaining promising results. |
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Merida; Mexico; December 2016 |
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Springer International Publishing |
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978-3-319-49054-0 |
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S+SSPR |
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DAG; 600.097; 602.006 |
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no |
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Admin @ si @ TSF2016 |
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2877 |
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Author |
Veronica Romero; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez |
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Title |
Information Extraction in Handwritten Marriage Licenses Books Using the MGGI Methodology |
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Conference Article |
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2017 |
Publication |
8th Iberian Conference on Pattern Recognition and Image Analysis |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
10255 |
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287-294 |
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Keywords |
Handwritten Text Recognition; Information extraction; Language modeling; MGGI; Categories-based language model |
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Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demographic and genealogical research. For example, marriage license books have been used for centuries by ecclesiastical and secular institutions to register marriages. These books follow a simple structure of the text in the records with a evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. In previous works we studied the use of category-based language models and how a Grammatical Inference technique known as MGGI could improve the accuracy of these tasks. In this work we analyze the main causes of the semantic errors observed in previous results and apply a better implementation of the MGGI technique to solve these problems. Using the resulting language model, transcription and information extraction experiments have been carried out, and the results support our proposed approach. |
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Faro; Portugal; June 2017 |
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L.A. Alexandre; J.Salvador Sanchez; Joao M. F. Rodriguez |
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978-3-319-58837-7 |
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IbPRIA |
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Notes |
DAG; 602.006; 600.097; 600.121 |
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no |
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Call Number |
Admin @ si @ RFV2017 |
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2952 |
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Author |
Pau Riba; Josep Llados; Alicia Fornes |
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Title |
Error-tolerant coarse-to-fine matching model for hierarchical graphs |
Type |
Conference Article |
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Year |
2017 |
Publication |
11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
10310 |
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107-117 |
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Graph matching; Hierarchical graph; Graph-based representation; Coarse-to-fine matching |
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Graph-based representations are effective tools to capture structural information from visual elements. However, retrieving a query graph from a large database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose to generate a hierarchy able to deal with different levels of abstraction while keeping information about the topology. For the proposed representations, a coarse-to-fine matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting. |
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Anacapri; Italy; May 2017 |
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Springer International Publishing |
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Pasquale Foggia; Cheng-Lin Liu; Mario Vento |
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GbRPR |
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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 @ RLF2017a |
Serial |
2951 |
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Author |
Alicia Fornes; Bart Lamiroy |
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Title |
Graphics Recognition, Current Trends and Evolutions |
Type |
Book Whole |
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2018 |
Publication |
Graphics Recognition, Current Trends and Evolutions |
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11009 |
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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. |
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Springer International Publishing |
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978-3-030-02283-9 |
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DAG; 600.121 |
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no |
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Admin @ si @ FoL2018 |
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3171 |
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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 ![sorted by Volume (numeric) field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
11009 |
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81-95 |
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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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no |
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Admin @ si @ BRC2018 |
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3227 |
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Author |
Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
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Title |
Learning to Learn from Web Data through Deep Semantic Embeddings |
Type |
Conference Article |
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Year |
2018 |
Publication |
15th European Conference on Computer Vision Workshops |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
11134 |
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514-529 |
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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. |
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Munich; Alemanya; September 2018 |
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ECCVW |
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Notes |
DAG; 600.129; 601.338; 600.121 |
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no |
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Call Number |
Admin @ si @ GGG2018a |
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3175 |
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Permanent link to this record |
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Author |
Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
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Title |
Learning from# Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods |
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Conference Article |
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Year |
2018 |
Publication |
15th European Conference on Computer Vision Workshops |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
11134 |
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530-544 |
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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. |
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Munich; Alemanya; September 2018 |
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ECCVW |
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DAG; 600.129; 601.338; 600.121 |
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no |
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Admin @ si @ GGG2018b |
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3176 |
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Author |
Lluis Gomez; Andres Mafla; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
Single Shot Scene Text Retrieval |
Type |
Conference Article |
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Year |
2018 |
Publication |
15th European Conference on Computer Vision |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
11218 |
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728-744 |
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Image retrieval; Scene text; Word spotting; Convolutional Neural Networks; Region Proposals Networks; PHOC |
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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. |
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Munich; September 2018 |
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ECCV |
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Notes |
DAG; 600.084; 601.338; 600.121; 600.129 |
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no |
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Call Number |
Admin @ si @ GMR2018 |
Serial |
3143 |
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Author |
Anguelos Nicolaou; Sounak Dey; V.Christlein; A.Maier; Dimosthenis Karatzas |
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Title |
Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings |
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Conference Article |
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2018 |
Publication |
International Workshop on Reproducible Research in Pattern Recognition |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
11455 |
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71-82 |
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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. |
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DAG; 600.121; 600.129 |
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no |
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Call Number |
Admin @ si @ NDC2018 |
Serial |
3178 |
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