Pyramidal Stochastic Graphlet Embedding for Document Pattern Classification

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordDocument pattern classification methods using graphs have received a lot of attention because of its robust representation paradigm and rich theoretical background. However, the way of preserving and the process for delineating documents with graphs introduce noise in the rendition of underlying data, which creates instability in the graph representation. To deal with such unreliability in representation, in this paper, we propose Pyramidal Stochastic Graphlet Embedding (PSGE). Given a graph representing a document pattern, our method first computes a graph pyramid by successively reducing the base graph. Once the graph pyramid is computed, we apply Stochastic Graphlet Embedding (SGE) for each level of the pyramid and combine their embedded representation to obtain a global delineation of the original graph. The consideration of pyramid of graphs rather than just a base graph extends the representational power of the graph embedding, which reduces the instability caused due to noise and distortion. When plugged with support vector machine, our proposed PSGE has outperformed the state-of-The-art results in recognition of handwritten words as well as graphical symbols.European Union Horizon 2020Ministerio de Educación, Cultura y Deporte, SpainRamon y Cajal FellowshipCERCA Program/Generalitat de Cataluny

Graph-Based Deep Learning for Graphics Classification

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordGraph-based representations are a common way to deal with graphics recognition problems. However, previous works were mainly focused on developing learning-free techniques. The success of deep learning frameworks have proved that learning is a powerful tool to solve many problems, however it is not straightforward to extend these methodologies to non euclidean data such as graphs. On the other hand, graphs are a good representational structure for graphical entities. In this work, we present some deep learning techniques that have been proposed in the literature for graph-based representations and we show how they can be used in graphics recognition problems.European Union Horizon 2020FPUMinisterio de Educación, Cultura y Deporte, SpainRamon y Cajal FellowshipCERCA Program/Generalitat de Cataluny

Improving Information Retrieval in Multiwriter Scenario by Exploiting the Similarity Graph of Document Terms

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordInformation Retrieval (IR) is the activity of obtaining information resources relevant to a questioned information. It usually retrieves a set of objects ranked according to the relevancy to the needed fact. In document analysis, information retrieval receives a lot of attention in terms of symbol and word spotting. However, through decades the community mostly focused either on printed or on single writer scenario, where the state-of-The-art results have achieved reasonable performance on the available datasets. Nevertheless, the existing algorithms do not perform accordingly on multiwriter scenario. A graph representing relations between a set of objects is a structure where each node delineates an individual element and the similarity between them is represented as a weight on the connecting edge. In this paper, we explore different analytics of graphs constructed from words or graphical symbols, such as diffusion, shortest path, etc. to improve the performance of information retrieval methods in multiwriter scenario.European Union Horizon 2020Ministerio de Educación, Cultura y Deporte, SpainFPUCERCA Programme/Generalitat de Cataluny

Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordIn 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.European Union Horizon 2020CERCA Programme/Generalitat de Cataluny

Entrepreneurial decisions: Insights into the use of support services for new business creation

This research presents an integrative model about the use of those services that have been specifically designed to support entrepreneurial initiative. By contrast with conventional perspectives from the entrepreneurship field, mainly drawn from a resource-based view, we propose a two-fold approach to explain the utilization of services that are oriented to new business creation: by considering the role of resources within the start-up's reach (internal and external); by incorporating a behavioral and decision-making approach. On the basis of the suggested decision-making framework, a multi-stage decision model is developed and tested by means of a representative sample of entrepreneurs linked to a local development agency. The results show that the adoption and use of support services for new business creation is a complex and reflexive process, triggered by the entrepreneur's internal forces.The entrepreneur searches for information throughout the process and, with assistance from internal teams and external networks, evaluates the choices of businesssupport services. Our findings offer relevant implications and recommendations for business incubators and institutions

Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition

Revised selected papers from Eighth IAPR International Workshop on Graphics RECognition (GREC) 2009.The motivation behind our work is to present a new methodology for symbol recognition. The proposed method employs a structural approach for representing visual associations in symbols and a statistical classifier for recognition. We vectorize a graphic symbol, encode its topological and geometrical information by an attributed relational graph and compute a signature from this structural graph. We have addressed the sensitivity of structural representations to noise, by using data adapted fuzzy intervals. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set. The Bayesian network is deployed in a supervised learning scenario for recognizing query symbols. The method has been evaluated for robustness against degradations & deformations on pre-segmented 2D linear architectural & electronic symbols from GREC databases, and for its recognition abilities on symbols with context noise i.e. cropped symbols

Baseline characteristics of an incident haemodialysis population in Spain: results from ANSWER—a multicentre, prospective, observational cohort study

Background. The ANSWER study aims to identify risk factors leading to increased cardiovascular morbidity and mortality in a Spanish incident haemodialysis population. This paper summarizes the baseline characteristics of this population