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Author Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva edit  doi
openurl 
  Title (down) Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes Type Journal Article
  Year 2009 Publication Journal of Signal Processing Systems Abbreviated Journal  
  Volume 55 Issue 1-3 Pages 35–47  
  Keywords  
  Abstract Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this sense, error-correcting output codes (ECOC) show to robustly combine binary classifiers to solve multi-class problems. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different sub-sets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers. Furthermore, the combination of RF and texture-based features also shows improvements over the state-of-the-art approaches.  
  Address  
  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 1939-8018 ISBN Medium  
  Area Expedition Conference  
  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ EPM2009 Serial 1258  
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Author Sergio Escalera; Ana Puig; Oscar Amoros; Maria Salamo edit  doi
openurl 
  Title (down) Intelligent GPGPU Classification in Volume Visualization: a framework based on Error-Correcting Output Codes Type Journal Article
  Year 2011 Publication Computer Graphics Forum Abbreviated Journal CGF  
  Volume 30 Issue 7 Pages 2107-2115  
  Keywords  
  Abstract IF JCR 1.455 2010 25/99
In volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on-demand multiple regions of interest. These methods can be split into a two-level GPU-based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error-Correcting Output Codes (ECOC) framework. In a pre-processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre-labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi-class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU-OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy.
 
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  Area Expedition Conference  
  Notes MILAB; HuPBA Approved no  
  Call Number Admin @ si @ EPA2011 Serial 1881  
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Author Jelena Gorbova; Egils Avots; Iiris Lusi; Mark Fishel; Sergio Escalera; Gholamreza Anbarjafari edit  doi
openurl 
  Title (down) Integrating Vision and Language for First Impression Personality Analysis Type Journal Article
  Year 2018 Publication IEEE Multimedia Abbreviated Journal MULTIMEDIA  
  Volume 25 Issue 2 Pages 24 - 33  
  Keywords  
  Abstract The authors present a novel methodology for analyzing integrated audiovisual signals and language to assess a persons personality. An evaluation of their proposed multimodal method using a job candidate screening system that predicted five personality traits from a short video demonstrates the methods effectiveness.  
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  Area Expedition Conference  
  Notes HUPBA; 602.133 Approved no  
  Call Number Admin @ si @ GAL2018 Serial 3124  
Permanent link to this record
 

 
Author Maria Salamo; Sergio Escalera edit  openurl
  Title (down) Increasing Retrieval Quality in Conversational Recommenders Type Journal Article
  Year 2011 Publication IEEE Transactions on Knowledge and Data Engineering Abbreviated Journal TKDE  
  Volume 99 Issue Pages 1-1  
  Keywords  
  Abstract IF JCR CCIA 2.286 2009 24/103
JCR Impact Factor 2010: 1.851
A major task of research in conversational recommender systems is personalization. Critiquing is a common and powerful form of feedback, where a user can express her feature preferences by applying a series of directional critiques over the recommendations instead of providing specific preference values. Incremental Critiquing is a conversational recommender system that uses critiquing as a feedback to efficiently personalize products. The expectation is that in each cycle the system retrieves the products that best satisfy the user’s soft product preferences from a minimal information input. In this paper, we present a novel technique that increases retrieval quality based on a combination of compatibility and similarity scores. Under the hypothesis that a user learns Turing the recommendation process, we propose two novel exponential reinforcement learning approaches for compatibility that take into account both the instant at which the user makes a critique and the number of satisfied critiques. Moreover, we consider that the impact of features on the similarity differs according to the preferences manifested by the user. We propose a global weighting approach that uses a common weight for nearest cases in order to focus on groups of relevant products. We show that our methodology significantly improves recommendation efficiency in four data sets of different sizes in terms of session length in comparison with state-of-the-art approaches. Moreover, our recommender shows higher robustness against noisy user data when compared to classical approaches
 
  Address  
  Corporate Author Thesis  
  Publisher IEEE Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1041-4347 ISBN Medium  
  Area Expedition Conference  
  Notes MILAB; HuPBA Approved no  
  Call Number Admin @ si @ SaE2011 Serial 1713  
Permanent link to this record
 

 
Author Marc Oliu; Ciprian Corneanu; Kamal Nasrollahi; Olegs Nikisins; Sergio Escalera; Yunlian Sun; Haiqing Li; Zhenan Sun; Thomas B. Moeslund; Modris Greitans edit  url
openurl 
  Title (down) Improved RGB-D-T based Face Recognition Type Journal Article
  Year 2016 Publication IET Biometrics Abbreviated Journal BIO  
  Volume 5 Issue 4 Pages 297 - 303  
  Keywords  
  Abstract Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent years. At the same time a multimodal facial recognition is a promising approach. This study combines the latest successes in both directions by applying deep learning convolutional neural networks (CNN) to the multimodal RGB, depth, and thermal (RGB-D-T) based facial recognition problem outperforming previously published results. Furthermore, a late fusion of the CNN-based recognition block with various hand-crafted features (local binary patterns, histograms of oriented gradients, Haar-like rectangular features, histograms of Gabor ordinal measures) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this study show that the classical engineered features and CNN-based features can complement each other for recognition purposes.  
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  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA;MILAB; Approved no  
  Call Number Admin @ si @ OCN2016 Serial 2854  
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