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Author Hugo Jair Escalante; Victor Ponce; Sergio Escalera; Xavier Baro; Alicia Morales-Reyes; Jose Martinez-Carranza edit   pdf
doi  openurl
  Title Evolving weighting schemes for the Bag of Visual Words Type Journal Article
  Year 2017 Publication Neural Computing and Applications Abbreviated Journal Neural Computing and Applications  
  Volume 28 Issue 5 Pages (down) 925–939  
  Keywords Bag of Visual Words; Bag of features; Genetic programming; Term-weighting schemes; Computer vision  
  Abstract The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved
to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g.,term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the
performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from
scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method.
 
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor Springer  
  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;MV; no menciona;OR;MILAB Approved no  
  Call Number Admin @ si @ EPE2017 Serial 2743  
Permanent link to this record
 

 
Author Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guçlu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia Liem; Marcel A. J. Van Gerven; Rob Van Lier edit   pdf
url  doi
openurl 
  Title Modeling, Recognizing, and Explaining Apparent Personality from Videos Type Journal Article
  Year 2022 Publication IEEE Transactions on Affective Computing Abbreviated Journal TAC  
  Volume 13 Issue 2 Pages (down) 894-911  
  Keywords  
  Abstract Explainability and interpretability are two critical aspects of decision support systems. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of apparent personality recognition. To the best of our knowledge, this is the first effort in this direction. We describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. We investigate the issue of bias in detail. Finally, derived from our study, we outline research opportunities that we foresee will be relevant in this area in the near future.  
  Address 1 April-June 2022  
  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 menciona;MV;OR;MILAB Approved no  
  Call Number Admin @ si @ EKS2022 Serial 3406  
Permanent link to this record
 

 
Author Miguel Angel Bautista; Sergio Escalera; Oriol Pujol edit   pdf
doi  openurl
  Title On the Design of an ECOC-Compliant Genetic Algorithm Type Journal Article
  Year 2014 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 47 Issue 2 Pages (down) 865-884  
  Keywords  
  Abstract Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches.  
  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;MILAB Approved no  
  Call Number Admin @ si @ BEP2013 Serial 2254  
Permanent link to this record
 

 
Author Jose Garcia-Rodriguez; Isabelle Guyon; Sergio Escalera; Alexandra Psarrou; Andrew Lewis; Miguel Cazorla edit  doi
openurl 
  Title Editorial: Special Issue on Computational Intelligence for Vision and Robotics Type Journal Article
  Year 2017 Publication Neural Computing and Applications Abbreviated Journal Neural Computing and Applications  
  Volume 28 Issue 5 Pages (down) 853–854  
  Keywords  
  Abstract  
  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;MILAB; no menciona Approved no  
  Call Number Admin @ si @ GGE2017 Serial 2845  
Permanent link to this record
 

 
Author 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 (down) 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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