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Author (up) Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera edit  url
doi  openurl
  Title A Genetic-based Subspace Analysis Method for Improving Error-Correcting Output Coding Type Journal Article
  Year 2013 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 46 Issue 10 Pages 2830-2839  
  Keywords Error Correcting Output Codes; Evolutionary computation; Multiclass classification; Feature subspace; Ensemble classification  
  Abstract Two key factors affecting the performance of Error Correcting Output Codes (ECOC) in multiclass classification problems are the independence of binary classifiers and the problem-dependent coding design. In this paper, we propose an evolutionary algorithm-based approach to the design of an application-dependent codematrix in the ECOC framework. The central idea of this work is to design a three-dimensional codematrix, where the third dimension is the feature space of the problem domain. In order to do that, we consider the feature space in the design process of the codematrix with the aim of improving the independence and accuracy of binary classifiers. The proposed method takes advantage of some basic concepts of ensemble classification, such as diversity of classifiers, and also benefits from the evolutionary approach for optimizing the three-dimensional codematrix, taking into account the problem domain. We provide a set of experimental results using a set of benchmark datasets from the UCI Machine Learning Repository, as well as two real multiclass Computer Vision problems. Both sets of experiments are conducted using two different base learners: Neural Networks and Decision Trees. The results show that the proposed method increases the classification accuracy in comparison with the state-of-the-art ECOC coding techniques.  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0031-3203 ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ BGE2013a Serial 2247  
Permanent link to this record
 

 
Author (up) Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera edit  doi
openurl 
  Title Combining Local and Global Learners in the Pairwise Multiclass Classification Type Journal Article
  Year 2015 Publication Pattern Analysis and Applications Abbreviated Journal PAA  
  Volume 18 Issue 4 Pages 845-860  
  Keywords Multiclass classification; Pairwise approach; One-versus-one  
  Abstract Pairwise classification is a well-known class binarization technique that converts a multiclass problem into a number of two-class problems, one problem for each pair of classes. However, in the pairwise technique, nuisance votes of many irrelevant classifiers may result in a wrong class prediction. To overcome this problem, a simple, but efficient method is proposed and evaluated in this paper. The proposed method is based on excluding some classes and focusing on the most probable classes in the neighborhood space, named Local Crossing Off (LCO). This procedure is performed by employing a modified version of standard K-nearest neighbor and large margin nearest neighbor algorithms. The LCO method takes advantage of nearest neighbor classification algorithm because of its local learning behavior as well as the global behavior of powerful binary classifiers to discriminate between two classes. Combining these two properties in the proposed LCO technique will avoid the weaknesses of each method and will increase the efficiency of the whole classification system. On several benchmark datasets of varying size and difficulty, we found that the LCO approach leads to significant improvements using different base learners. The experimental results show that the proposed technique not only achieves better classification accuracy in comparison to other standard approaches, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes.  
  Address  
  Corporate Author Thesis  
  Publisher Springer London Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1433-7541 ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ BGE2014 Serial 2441  
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Author (up) Mohammad Naser Sabet; Pau Buch Cardona; Egils Avots; Kamal Nasrollahi; Sergio Escalera; Thomas B. Moeslund; Gholamreza Anbarjafari edit  url
doi  openurl
  Title Privacy-Constrained Biometric System for Non-cooperative Users Type Journal Article
  Year 2019 Publication Entropy Abbreviated Journal ENTROPY  
  Volume 21 Issue 11 Pages 1033  
  Keywords biometric recognition; multimodal-based human identification; privacy; deep learning  
  Abstract With the consolidation of the new data protection regulation paradigm for each individual within the European Union (EU), major biometric technologies are now confronted with many concerns related to user privacy in biometric deployments. When individual biometrics are disclosed, the sensitive information about his/her personal data such as financial or health are at high risk of being misused or compromised. This issue can be escalated considerably over scenarios of non-cooperative users, such as elderly people residing in care homes, with their inability to interact conveniently and securely with the biometric system. The primary goal of this study is to design a novel database to investigate the problem of automatic people recognition under privacy constraints. To do so, the collected data-set contains the subject’s hand and foot traits and excludes the face biometrics of individuals in order to protect their privacy. We carried out extensive simulations using different baseline methods, including deep learning. Simulation results show that, with the spatial features extracted from the subject sequence in both individual hand or foot videos, state-of-the-art deep models provide promising recognition performance.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA; no proj Approved no  
  Call Number Admin @ si @ NBA2019 Serial 3313  
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Author (up) O. Rodriguez-Leor; Carlo Gatta; E Fernandez-Nofrerias; Oriol Pujol; Neus Salvatella; C. Bosch; H. Tizon; Petia Radeva; J. Mauri edit  openurl
  Title Computationally Efficient Image-based IVUS Pullbacks Gating Type Journal
  Year 2008 Publication European Heart Journal, ESC Supplement, Munich, 2008, p. 775 Abbreviated Journal  
  Volume Issue Pages  
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  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
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  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ RGF2008 Serial 1036  
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Author (up) O. Rodriguez-Leor; E Fernandez-Nofrerias; J. Mauri; C. Garcia; R. Villuendas; V. Valle; Oriol Pujol; Petia Radeva edit  openurl
  Title Intravascular ultrasound segmentation using local binary patterns Type Journal
  Year 2003 Publication European Heart Journal (IF: 5.997), ESC Congress 2003 Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Vienna (Austria)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ RFM2003a Serial 407  
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