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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Marçal Rusiñol; David Aldavert; Dimosthenis Karatzas; Ricardo Toledo; Josep Llados |
![goto web page (via DOI) doi](img/doi.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Interactive Trademark Image Retrieval by Fusing Semantic and Visual Content. Advances in Information Retrieval |
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Conference Article |
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2011 |
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33rd European Conference on Information Retrieval |
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6611 |
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314-325 |
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In this paper we propose an efficient queried-by-example retrieval system which is able to retrieve trademark images by similarity from patent and trademark offices' digital libraries. Logo images are described by both their semantic content, by means of the Vienna codes, and their visual contents, by using shape and color as visual cues. The trademark descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time. The resulting ranked lists are combined by using the Condorcet method and a relevance feedback step helps to iteratively revise the query and refine the obtained results. The experiments demonstrate the effectiveness and efficiency of this system on a realistic and large dataset. |
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Dublin, Ireland |
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Springer |
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Berlin |
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P. Clough; C. Foley; C. Gurrin; G.J.F. Jones; W. Kraaij; H. Lee; V. Murdoch |
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978-3-642-20160-8 |
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DAG; RV;ADAS |
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Admin @ si @ RAK2011 |
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1737 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Marçal Rusiñol; Agnes Borras; Josep Llados |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Relational Indexing of Vectorial Primitives for Symbol Spotting in Line-Drawing Images |
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Journal Article |
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2010 |
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Pattern Recognition Letters |
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PRL |
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31 |
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3 |
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188–201 |
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Document image analysis and recognition, Graphics recognition, Symbol spotting ,Vectorial representations, Line-drawings |
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This paper presents a symbol spotting approach for indexing by content a database of line-drawing images. As line-drawings are digital-born documents designed by vectorial softwares, instead of using a pixel-based approach, we present a spotting method based on vector primitives. Graphical symbols are represented by a set of vectorial primitives which are described by an off-the-shelf shape descriptor. A relational indexing strategy aims to retrieve symbol locations into the target documents by using a combined numerical-relational description of 2D structures. The zones which are likely to contain the queried symbol are validated by a Hough-like voting scheme. In addition, a performance evaluation framework for symbol spotting in graphical documents is proposed. The presented methodology has been evaluated with a benchmarking set of architectural documents achieving good performance results. |
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DAG @ dag @ RBL2010 |
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1177 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Marçal Rusiñol |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
A Model of Vectorial Signatures in Terms of Expressive Sub-Shapes: Symbol Indexation in Technical Documents |
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2006 |
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CVC Technical Report #94 |
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CVC (UAB) |
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DAG @ dag @ Rus2006 |
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668 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Marçal Rusiñol |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Geometric and Structural-based Symbol Spotting. Application to Focused Retrieval in Graphic Document Collections |
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2009 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Usually, pattern recognition systems consist of two main parts. On the one hand, the data acquisition and, on the other hand, the classification of this data on a certain category. In order to recognize which category a certain query element belongs to, a set of pattern models must be provided beforehand. An off-line learning stage is needed to train the classifier and to offer a robust classification of the patterns. Within the pattern recognition field, we are interested in the recognition of graphics and, in particular, on the analysis of documents rich in graphical information. In this context, one of the main concerns is to see if the proposed systems remain scalable with respect to the data volume so as it can handle growing amounts of symbol models. In order to avoid to work with a database of reference symbols, symbol spotting and on-the-fly symbol recognition methods have been introduced in the past years.
Generally speaking, the symbol spotting problem can be defined as the identification of a set of regions of interest from a document image which are likely to contain an instance of a certain queriedn symbol without explicitly applying the whole pattern recognition scheme. Our application framework consists on indexing a collection of graphic-rich document images. This collection is
queried by example with a single instance of the symbol to look for and, by means of symbol spotting methods we retrieve the regions of interest where the symbol is likely to appear within the documents. This kind of applications are known as focused retrieval methods.
In order that the focused retrieval application can handle large collections of documents there is a need to provide an efficient access to the large volume of information that might be stored. We use indexing strategies in order to efficiently retrieve by similarity the locations where a certain part of the symbol appears. In that scenario, graphical patterns should be used as indices for accessing and navigating the collection of documents.
These indexing mechanism allow the user to search for similar elements using graphical information rather than textual queries.
Along this thesis we present a spotting architecture and different methods aiming to build a complete focused retrieval application dealing with a graphic-rich document collections. In addition, a protocol to evaluate the performance of symbol
spotting systems in terms of recognition abilities, location accuracy and scalability is proposed. |
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Barcelona (Spain) |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Josep Llados |
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DAG |
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DAG @ dag @ Rus2009 |
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1264 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Marçal Rusiñol |
![goto web page url](img/www.gif)
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Title |
Classificació semàntica i visual de documents digitals |
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2019 |
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Revista de biblioteconomia i documentacio |
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75-86 |
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Se analizan los sistemas de procesamiento automático que trabajan sobre documentos digitalizados con el objetivo de describir los contenidos. De esta forma contribuyen a facilitar el acceso, permitir la indización automática y hacer accesibles los documentos a los motores de búsqueda. El objetivo de estas tecnologías es poder entrenar modelos computacionales que sean capaces de clasificar, agrupar o realizar búsquedas sobre documentos digitales. Así, se describen las tareas de clasificación, agrupamiento y búsqueda. Cuando utilizamos tecnologías de inteligencia artificial en los sistemas de
clasificación esperamos que la herramienta nos devuelva etiquetas semánticas; en sistemas de agrupamiento que nos devuelva documentos agrupados en clusters significativos; y en sistemas de búsqueda esperamos que dada una consulta, nos devuelva una lista ordenada de documentos en función de la relevancia. A continuación se da una visión de conjunto de los métodos que nos permiten describir los documentos digitales, tanto de manera visual (cuál es su apariencia), como a partir de sus contenidos semánticos (de qué hablan). En cuanto a la descripción visual de documentos se aborda el estado de la cuestión de las representaciones numéricas de documentos digitalizados
tanto por métodos clásicos como por métodos basados en el aprendizaje profundo (deep learning). Respecto de la descripción semántica de los contenidos se analizan técnicas como el reconocimiento óptico de caracteres (OCR); el cálculo de estadísticas básicas sobre la aparición de las diferentes palabras en un texto (bag-of-words model); y los métodos basados en aprendizaje profundo como el método word2vec, basado en una red neuronal que, dadas unas cuantas palabras de un texto, debe predecir cuál será la
siguiente palabra. Desde el campo de las ingenierías se están transfiriendo conocimientos que se han integrado en productos o servicios en los ámbitos de la archivística, la biblioteconomía, la documentación y las plataformas de gran consumo, sin embargo los algoritmos deben ser lo suficientemente eficientes no sólo para el reconocimiento y transcripción literal sino también para la capacidad de interpretación de los contenidos. |
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DAG; 600.084; 600.135; 600.121; 600.129 |
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Admin @ si @ Rus2019 |
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3282 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Manuel Graña; Bogdan Raducanu |
![goto web page (via DOI) doi](img/doi.gif)
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Special Issue on Bioinspired and knowledge based techniques and applications |
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Journal Article |
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2015 |
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Neurocomputing |
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NEUCOM |
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OR;MV |
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Admin @ si @ GrR2015 |
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2598 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Manuel Carbonell; Pau Riba; Mauricio Villegas; Alicia Fornes; Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents |
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Conference Article |
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2020 |
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25th International Conference on Pattern Recognition |
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The use of administrative documents to communicate and leave record of business information requires of methods
able to automatically extract and understand the content from
such documents in a robust and efficient way. In addition,
the semi-structured nature of these reports is specially suited
for the use of graph-based representations which are flexible
enough to adapt to the deformations from the different document
templates. Moreover, Graph Neural Networks provide the proper
methodology to learn relations among the data elements in
these documents. In this work we study the use of Graph
Neural Network architectures to tackle the problem of entity
recognition and relation extraction in semi-structured documents.
Our approach achieves state of the art results in the three
tasks involved in the process. Additionally, the experimentation
with two datasets of different nature demonstrates the good
generalization ability of our approach. |
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Virtual; January 2021 |
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ICPR |
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DAG; 600.121 |
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Admin @ si @ CRV2020 |
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3509 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Manuel Carbonell; Mauricio Villegas; Alicia Fornes; Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model |
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Conference Article |
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2018 |
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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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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 ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Manuel Carbonell; Joan Mas; Mauricio Villegas; Alicia Fornes; Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Title |
End-to-End Handwritten Text Detection and Transcription in Full Pages |
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Conference Article |
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2019 |
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2nd International Workshop on Machine Learning |
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5 |
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29-34 |
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Handwritten Text Recognition; Layout Analysis; Text segmentation; Deep Neural Networks; Multi-task learning |
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When transcribing handwritten document images, inaccuracies in the text segmentation step often cause errors in the subsequent transcription step. For this reason, some recent methods propose to perform the recognition at paragraph level. But still, errors in the segmentation of paragraphs can affect
the transcription performance. In this work, we propose an end-to-end framework to transcribe full pages. The joint text detection and transcription allows to remove the layout analysis requirement at test time. The experimental results show that our approach can achieve comparable results to models that assume
segmented paragraphs, and suggest that joining the two tasks brings an improvement over doing the two tasks separately. |
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Sydney; Australia; September 2019 |
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ICDAR WML |
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DAG; 600.140; 601.311; 600.140 |
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Admin @ si @ CMV2019 |
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3353 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Manuel Carbonell; Alicia Fornes; Mauricio Villegas; Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
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A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages |
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2020 |
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Pattern Recognition Letters |
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PRL |
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136 |
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219-227 |
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In the last years, the consolidation of deep neural network architectures for information extraction in document images has brought big improvements in the performance of each of the tasks involved in this process, consisting of text localization, transcription, and named entity recognition. However, this process is traditionally performed with separate methods for each task. In this work we propose an end-to-end model that combines a one stage object detection network with branches for the recognition of text and named entities respectively in a way that shared features can be learned simultaneously from the training error of each of the tasks. By doing so the model jointly performs handwritten text detection, transcription, and named entity recognition at page level with a single feed forward step. We exhaustively evaluate our approach on different datasets, discussing its advantages and limitations compared to sequential approaches. The results show that the model is capable of benefiting from shared features by simultaneously solving interdependent tasks. |
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DAG; 600.140; 601.311; 600.121 |
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Admin @ si @ CFV2020 |
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3451 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Manuel Carbonell |
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Neural Information Extraction from Semi-structured Documents A |
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2020 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Sectors as fintech, legaltech or insurance process an inflow of millions of forms, invoices, id documents, claims or similar every day. Together with these, historical archives provide gigantic amounts of digitized documents containing useful information that needs to be stored in machine encoded text with a meaningful structure. This procedure, known as information extraction (IE) comprises the steps of localizing and recognizing text, identifying named entities contained in it and optionally finding relationships among its elements. In this work we explore multi-task neural models at image and graph level to solve all steps in a unified way. While doing so we find benefits and limitations of these end-to-end approaches in comparison with sequential separate methods. More specifically, we first propose a method to produce textual as well as semantic labels with a unified model from handwritten text line images. We do so with the use of a convolutional recurrent neural model trained with connectionist temporal classification to predict the textual as well as semantic information encoded in the images. Secondly, motivated by the success of this approach we investigate the unification of the localization and recognition tasks of handwritten text in full pages with an end-to-end model, observing benefits in doing so. Having two models that tackle information extraction subsequent task pairs in an end-to-end to end manner, we lastly contribute with a method to put them all together in a single neural network to solve the whole information extraction pipeline in a unified way. Doing so we observe some benefits and some limitations in the approach, suggesting that in certain cases it is beneficial to train specialized models that excel at a single challenging task of the information extraction process, as it can be the recognition of named entities or the extraction of relationships between them. For this reason we lastly study the use of the recently arrived graph neural network architectures for the semantic tasks of the information extraction process, which are recognition of named entities and relation extraction, achieving promising results on the relation extraction part. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Alicia Fornes;Mauricio Villegas;Josep Llados |
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978-84-122714-1-6 |
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DAG; 600.121 |
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Admin @ si @ Car20 |
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3483 |
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Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde |
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Title |
Optimized CT-MR neurological image fusion framework using biologically inspired spiking neural model in hybrid ℓ1 - ℓ0 layer decomposition domain |
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Journal Article |
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2021 |
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Biomedical Signal Processing and Control |
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BSPC |
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68 |
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102535 |
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Medical image fusion plays an important role in the clinical diagnosis of several critical neurological diseases by merging complementary information available in multimodal images. In this paper, a novel CT-MR neurological image fusion framework is proposed using an optimized biologically inspired feedforward neural model in two-scale hybrid ℓ1 − ℓ0 decomposition domain using gray wolf optimization to preserve the structural as well as texture information present in source CT and MR images. Initially, the source images are subjected to two-scale ℓ1 − ℓ0 decomposition with optimized parameters, giving a scale-1 detail layer, a scale-2 detail layer and a scale-2 base layer. Two detail layers at scale-1 and 2 are fused using an optimized biologically inspired neural model and weighted average scheme based on local energy and modified spatial frequency to maximize the preservation of edges and local textures, respectively, while the scale-2 base layer gets fused using choose max rule to preserve the background information. To optimize the hyper-parameters of hybrid ℓ1 − ℓ0 decomposition and biologically inspired neural model, a fitness function is evaluated based on spatial frequency and edge index of the resultant fused image obtained by adding all the fused components. The fusion performance is analyzed by conducting extensive experiments on different CT-MR neurological images. Experimental results indicate that the proposed method provides better-fused images and outperforms the other state-of-the-art fusion methods in both visual and quantitative assessments. |
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Admin @ si @ DGR2021b |
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3636 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde |
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Title |
Multi-scale decomposition-based CT-MR neurological image fusion using optimized bio-inspired spiking neural model with meta-heuristic optimization |
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Journal Article |
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2021 |
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International Journal of Imaging Systems and Technology |
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IMA |
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31 |
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4 |
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2170-2188 |
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Multi-modal medical image fusion plays an important role in clinical diagnosis and works as an assistance model for clinicians. In this paper, a computed tomography-magnetic resonance (CT-MR) image fusion model is proposed using an optimized bio-inspired spiking feedforward neural network in different decomposition domains. First, source images are decomposed into base (low-frequency) and detail (high-frequency) layer components. Low-frequency subbands are fused using texture energy measures to capture the local energy, contrast, and small edges in the fused image. High-frequency coefficients are fused using firing maps obtained by pixel-activated neural model with the optimized parameters using three different optimization techniques such as differential evolution, cuckoo search, and gray wolf optimization, individually. In the optimization model, a fitness function is computed based on the edge index of resultant fused images, which helps to extract and preserve sharp edges available in the source CT and MR images. To validate the fusion performance, a detailed comparative analysis is presented among the proposed and state-of-the-art methods in terms of quantitative and qualitative measures along with computational complexity. Experimental results show that the proposed method produces a significantly better visual quality of fused images meanwhile outperforms the existing methods. |
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MILAB; no menciona |
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Admin @ si @ DGR2021a |
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3630 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Maedeh Aghaei; Petia Radeva |
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Title |
Bag-of-Tracklets for Person Tracking in Life-Logging Data |
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Conference Article |
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2014 |
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17th International Conference of the Catalan Association for Artificial Intelligence |
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269 |
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35-44 |
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By increasing popularity of wearable cameras, life-logging data analysis is becoming more and more important and useful to derive significant events out of this substantial collection of images. In this study, we introduce a new tracking method applied to visual life-logging, called bag-of-tracklets, which is based on detecting, localizing and tracking of people. Given the low spatial and temporal resolution of the image data, our model generates and groups tracklets in a unsupervised framework and extracts image sequences of person appearance according to a similarity score of the bag-of-tracklets. The model output is a meaningful sequence of events expressing human appearance and tracking them in life-logging data. The achieved results prove the robustness of our model in terms of efficiency and accuracy despite the low spatial and temporal resolution of the data. |
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978-1-61499-451-0 |
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CCIA |
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MILAB |
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Admin @ si @ AgR2015 |
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2607 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva |
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Title |
Towards social interaction detection in egocentric photo-streams |
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Conference Article |
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2015 |
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Proceedings of SPIE, 8th International Conference on Machine Vision , ICMV 2015 |
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9875 |
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Detecting social interaction in videos relying solely on visual cues is a valuable task that is receiving increasing attention in recent years. In this work, we address this problem in the challenging domain of egocentric photo-streams captured by a low temporal resolution wearable camera (2fpm). The major difficulties to be handled in this context are the sparsity of observations as well as unpredictability of camera motion and attention orientation due to the fact that the camera is worn as part of clothing. Our method consists of four steps: multi-faces localization and tracking, 3D localization, pose estimation and analysis of f-formations. By estimating pair-to-pair interaction probabilities over the sequence, our method states the presence or absence of interaction with the camera wearer and specifies which people are more involved in the interaction. We tested our method over a dataset of 18.000 images and we show its reliability on our considered purpose. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. |
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ICMV |
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MILAB |
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Admin @ si @ ADR2015a |
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2702 |
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