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Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados; Umapada Pal |
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Title |
Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch |
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2018 |
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24th International Conference on Pattern Recognition |
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916 - 921 |
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In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query. A cross-modal deep network architecture is formulated to jointly model the sketch and text input modalities as well as the the image output modality, learning a common embedding between text and images and between sketches and images. In addition, an attention model is used to selectively focus the attention on the different objects of the image, allowing for retrieval with multiple objects in the query. Experiments show that the proposed method performs the best in both single and multiple object image retrieval in standard datasets. |
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Beijing; China; August 2018 |
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DAG; 602.167; 602.168; 600.097; 600.084; 600.121; 600.129 |
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Admin @ si @ DDG2018b |
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3152 |
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Fernando Vilariño; Dimosthenis Karatzas; Alberto Valcarce |
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Title |
The Library Living Lab Barcelona: A participative approach to technology as an enabling factor for innovation in cultural spaces |
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2018 |
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Technology Innovation Management Review |
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DAG; MV; 600.097; 600.121; 600.129;SIAI |
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Admin @ si @ VKV2018a |
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3153 |
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Fernando Vilariño; Dimosthenis Karatzas; Alberto Valcarce |
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Title |
Libraries as New Innovation Hubs: The Library Living Lab |
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2018 |
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30th ISPIM Innovation Conference |
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Libraries are in deep transformation both in EU and around the world, and they are thriving within a great window of opportunity for innovation. In this paper, we show how the Library Living Lab in Barcelona participated of this changing scenario and contributed to create the Bibliolab program, where more than 200 public libraries give voice to their users in a global user-centric innovation initiative, using technology as enabling factor. The Library Living Lab is a real 4-helix implementation where Universities, Research Centers, Public Administration, Companies and the Neighbors are joint together to explore how technology transforms the cultural experience of people. This case is an example of scalability and provides reference tools for policy making, sustainability, user engage methodologies and governance. We provide specific examples of new prototypes and services that help to understand how to redefine the role of the Library as a real hub for social innovation. |
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Stockholm; May 2018 |
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ISPIM |
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DAG; MV; 600.097; 600.121; 600.129;SIAI |
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Admin @ si @ VKV2018b |
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3154 |
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Juan Ignacio Toledo; Manuel Carbonell; Alicia Fornes; Josep Llados |
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Title |
Information Extraction from Historical Handwritten Document Images with a Context-aware Neural Model |
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2019 |
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Pattern Recognition |
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PR |
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86 |
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27-36 |
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Document image analysis; Handwritten documents; Named entity recognition; Deep neural networks |
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Many historical manuscripts that hold trustworthy memories of the past societies contain information organized in a structured layout (e.g. census, birth or marriage records). The precious information stored in these documents cannot be effectively used nor accessed without costly annotation efforts. The transcription driven by the semantic categories of words is crucial for the subsequent access. In this paper we describe an approach to extract information from structured historical handwritten text images and build a knowledge representation for the extraction of meaning out of historical data. The method extracts information, such as named entities, without the need of an intermediate transcription step, thanks to the incorporation of context information through language models. Our system has two variants, the first one is based on bigrams, whereas the second one is based on recurrent neural networks. Concretely, our second architecture integrates a Convolutional Neural Network to model visual information from word images together with a Bidirecitonal Long Short Term Memory network to model the relation among the words. This integrated sequential approach is able to extract more information than just the semantic category (e.g. a semantic category can be associated to a person in a record). Our system is generic, it deals with out-of-vocabulary words by design, and it can be applied to structured handwritten texts from different domains. The method has been validated with the ICDAR IEHHR competition protocol, outperforming the existing approaches. |
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DAG; 600.097; 601.311; 603.057; 600.084; 600.140; 600.121 |
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Admin @ si @ TCF2019 |
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3166 |
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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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2018 |
Publication |
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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DAG; 600.097; 603.057; 302.065; 601.302; 600.084; 600.121; 600.129 |
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Admin @ si @ KTR2018 |
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3167 |
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Author |
Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes |
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Title |
Learning Graph Distances with Message Passing Neural Networks |
Type |
Conference Article |
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Year |
2018 |
Publication |
24th International Conference on Pattern Recognition |
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2239-2244 |
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★Best Paper Award★ |
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Graph representations have been widely used in pattern recognition thanks to their powerful representation formalism and rich theoretical background. A number of error-tolerant graph matching algorithms such as graph edit distance have been proposed for computing a distance between two labelled graphs. However, they typically suffer from a high
computational complexity, which makes it difficult to apply
these matching algorithms in a real scenario. In this paper, we propose an efficient graph distance based on the emerging field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure and learns a metric with a siamese network approach. The performance of the proposed graph distance is validated in two application cases, graph classification and graph retrieval of handwritten words, and shows a promising performance when compared with
(approximate) graph edit distance benchmarks. |
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Beijing; China; August 2018 |
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DAG; 600.097; 603.057; 601.302; 600.121 |
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Admin @ si @ RFL2018 |
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3168 |
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Author |
Jialuo Chen; Pau Riba; Alicia Fornes; Juan Mas; Josep Llados; Joana Maria Pujadas-Mora |
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Title |
Word-Hunter: A Gamesourcing Experience to Validate the Transcription of Historical Manuscripts |
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Conference Article |
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2018 |
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16th International Conference on Frontiers in Handwriting Recognition |
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528-533 |
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Crowdsourcing; Gamification; Handwritten documents; Performance evaluation |
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Nowadays, there are still many handwritten historical documents in archives waiting to be transcribed and indexed. Since manual transcription is tedious and time consuming, the automatic transcription seems the path to follow. However, the performance of current handwriting recognition techniques is not perfect, so a manual validation is mandatory. Crowdsourcing is a good strategy for manual validation, however it is a tedious task. In this paper we analyze experiences based in gamification
in order to propose and design a gamesourcing framework that increases the interest of users. Then, we describe and analyze our experience when validating the automatic transcription using the gamesourcing application. Moreover, thanks to the combination of clustering and handwriting recognition techniques, we can speed up the validation while maintaining the performance. |
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Niagara Falls, USA; August 2018 |
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ICFHR |
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DAG; 600.097; 603.057; 600.121 |
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Admin @ si @ CRF2018 |
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3169 |
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Manuel Carbonell; Mauricio Villegas; Alicia Fornes; Josep Llados |
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Title |
Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model |
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2018 |
Publication |
13th IAPR International Workshop on Document Analysis Systems |
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399-404 |
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Named entity recognition; Handwritten Text Recognition; neural networks |
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When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks. This has the disadvantage that errors in the first module affect heavily the
performance of the second module. In this work we propose to do both tasks jointly, using a single neural network with a common architecture used for plain text recognition. Experimentally, the work has been tested on a collection of historical marriage records. Results of experiments are presented to show the effect on the performance for different
configurations: different ways of encoding the information, doing or not transfer learning and processing at text line or multi-line region level. The results are comparable to state of the art reported in the ICDAR 2017 Information Extraction competition, even though the proposed technique does not use any dictionaries, language modeling or post processing. |
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Vienna; Austria; April 2018 |
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DAS |
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DAG; 600.097; 603.057; 601.311; 600.121 |
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Admin @ si @ CVF2018 |
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3170 |
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Author |
Katerine Diaz; Jesus Martinez del Rincon; Marçal Rusiñol; Aura Hernandez-Sabate |
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Title |
Feature Extraction by Using Dual-Generalized Discriminative Common Vectors |
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2019 |
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Journal of Mathematical Imaging and Vision |
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JMIV |
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61 |
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3 |
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331-351 |
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Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning |
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In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive methods. |
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DAG; ADAS; 600.084; 600.118; 600.121; 600.129 |
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Admin @ si @ DRR2019 |
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3172 |
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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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2018 |
Publication |
15th European Conference on Computer Vision Workshops |
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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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Admin @ si @ GGG2018b |
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3176 |
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