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| Author | Wenjuan Gong; W.Zhang; Jordi Gonzalez; Y.Ren; Z.Li | ||||
| Title | Enhanced Asymmetric Bilinear Model for Face Recognition | Type | Journal Article | ||
| Year | 2015 | Publication | International Journal of Distributed Sensor Networks | Abbreviated Journal | IJDSN |
| Volume | Issue | Pages | Article ID 218514 | ||
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| Abstract | Bilinear models have been successfully applied to separate two factors, for example, pose variances and different identities in face recognition problems. Asymmetric model is a type of bilinear model which models a system in the most concise way. But seldom there are works exploring the applications of asymmetric bilinear model on face recognition problem with illumination changes. In this work, we propose enhanced asymmetric model for illumination-robust face recognition. Instead of initializing the factor probabilities randomly, we initialize them with nearest neighbor method and optimize them for the test data. Above that, we update the factor model to be identified. We validate the proposed method on a designed data sample and extended Yale B dataset. The experiment results show that the enhanced asymmetric models give promising results and good recognition accuracies. | ||||
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| Notes | ISE; 600.063; 600.078 | Approved | no | ||
| Call Number | Admin @ si @ GZG2015 | Serial | 2592 | ||
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| Author | Adriana Romero; Nicolas Ballas; Samira Ebrahimi Kahou; Antoine Chassang; Carlo Gatta; Yoshua Bengio | ||||
| Title | FitNets: Hints for Thin Deep Nets | Type | Conference Article | ||
| Year | 2015 | Publication | 3rd International Conference on Learning Representations ICLR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Computer Science ; Learning; Computer Science ;Neural and Evolutionary Computing | ||||
| Abstract | While depth tends to improve network performances, it also makes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed knowledge distillation approach is aimed at obtaining small and fast-to-execute models, and it has shown that a student network could imitate the soft output of a larger teacher network or ensemble of networks. In this paper, we extend this idea to allow the training of a student that is deeper and thinner than the teacher, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student. Because the student intermediate hidden layer will generally be smaller than the teacher's intermediate hidden layer, additional parameters are introduced to map the student hidden layer to the prediction of the teacher hidden layer. This allows one to train deeper students that can generalize better or run faster, a trade-off that is controlled by the chosen student capacity. For example, on CIFAR-10, a deep student network with almost 10.4 times less parameters outperforms a larger, state-of-the-art teacher network. | ||||
| Address | San Diego; CA; May 2015 | ||||
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| Area | Expedition | Conference | ICLR | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ RBK2015 | Serial | 2593 | ||
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| Author | Adriana Romero; Petia Radeva; Carlo Gatta | ||||
| Title | Meta-parameter free unsupervised sparse feature learning | Type | Journal Article | ||
| Year | 2015 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 37 | Issue | 8 | Pages | 1716-1722 |
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| Abstract | We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on CIFAR-10, STL- 10 and UCMerced show that the method achieves the state-of-theart performance, providing discriminative features that generalize well. | ||||
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| Notes | MILAB; 600.068; 600.079; 601.160 | Approved | no | ||
| Call Number | Admin @ si @ RRG2014b | Serial | 2594 | ||
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| Author | Manuel Graña; Bogdan Raducanu | ||||
| Title | Special Issue on Bioinspired and knowledge based techniques and applications | Type | Journal Article | ||
| Year | 2015 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
| Volume | Issue | Pages | 1-3 | ||
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| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ GrR2015 | Serial | 2598 | ||
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| Author | Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika | ||||
| Title | Facial Expression Recognition based on Multi-view Observations with Application to Social Robotics | Type | Conference Article | ||
| Year | 2014 | Publication | 1st Workshop on Computer Vision for Affective Computing | Abbreviated Journal | |
| Volume | Issue | Pages | 1-8 | ||
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| Abstract | Human-robot interaction is a hot topic nowadays in the social robotics community. One crucial aspect is represented by the affective communication which comes encoded through the facial expressions. In this paper, we propose a novel approach for facial expression recognition, which exploits an efficient and adaptive graph-based label propagation (semi-supervised mode) in a multi-observation framework. The facial features are extracted using an appearance-based 3D face tracker, view- and texture independent. Our method has been extensively tested on the CMU dataset, and has been conveniently compared with other methods for graph construction. With the proposed approach, we developed an application for an AIBO robot, in which it mirrors the recognized facial
expression. |
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| Address | Singapore; November 2014 | ||||
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| Area | Expedition | Conference | ACCV | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ RBD2014 | Serial | 2599 | ||
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| Author | C. Alejandro Parraga | ||||
| Title | Perceptual Psychophysics | Type | Book Chapter | ||
| Year | 2015 | Publication | Biologically-Inspired Computer Vision: Fundamentals and Applications | Abbreviated Journal | |
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| Publisher | Place of Publication | Editor | G.Cristobal; M.Keil; L.Perrinet | ||
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ISBN | 978-3-527-41264-8 | Medium | ||
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| Notes | CIC; 600.074 | Approved | no | ||
| Call Number | Admin @ si @ Par2015 | Serial | 2600 | ||
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| Author | Firat Ismailoglu; Ida G. Sprinkhuizen-Kuyper; Evgueni Smirnov; Sergio Escalera; Ralf Peeters | ||||
| Title | Fractional Programming Weighted Decoding for Error-Correcting Output Codes | Type | Conference Article | ||
| Year | 2015 | Publication | Multiple Classifier Systems, Proceedings of 12th International Workshop , MCS 2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 38-50 | ||
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| Abstract | In order to increase the classification performance obtained using Error-Correcting Output Codes designs (ECOC), introducing weights in the decoding phase of the ECOC has attracted a lot of interest. In this work, we present a method for ECOC designs that focuses on increasing hypothesis margin on the data samples given a base classifier. While achieving this, we implicitly reward the base classifiers with high performance, whereas punish those with low performance. The resulting objective function is of the fractional programming type and we deal with this problem through the Dinkelbach’s Algorithm. The conducted tests over well known UCI datasets show that the presented method is superior to the unweighted decoding and that it outperforms the results of the state-of-the-art weighted decoding methods in most of the performed experiments. | ||||
| Address | Gunzburg; Germany; June 2015 | ||||
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| Publisher | Springer International Publishing | Place of Publication | Editor | ||
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ISBN | 978-3-319-20247-1 | Medium | ||
| Area | Expedition | Conference | MCS | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ ISS2015 | Serial | 2601 | ||
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| Author | Hugo Jair Escalante; Jose Martinez; Sergio Escalera; Victor Ponce; Xavier Baro | ||||
| Title | Improving Bag of Visual Words Representations with Genetic Programming | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE International Joint Conference on Neural Networks IJCNN2015 | Abbreviated Journal | |
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| Abstract | The bag of visual words is a well established representation in diverse computer vision problems. Taking inspiration from the fields of text mining and retrieval, this representation has proved to be very effective in a large number of domains.
In most cases, a standard term-frequency weighting scheme is considered for representing images and videos in computer vision. This is somewhat surprising, as there are many alternative ways of generating bag of words representations within the text processing community. This paper explores the use of alternative weighting schemes for landmark tasks in computer vision: image categorization and gesture recognition. We study the suitability of using well-known supervised and unsupervised weighting schemes for such tasks. More importantly, we devise a genetic program that learns new ways of representing images and videos under the bag of visual words representation. The proposed method learns to combine term-weighting primitives trying to maximize the classification performance. Experimental results are reported in standard image and video data sets showing the effectiveness of the proposed evolutionary algorithm. |
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| Address | Killarney; Ireland; July 2015 | ||||
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| Area | Expedition | Conference | IJCNN | ||
| Notes | HuPBA;MV | Approved | no | ||
| Call Number | Admin @ si @ EME2015 | Serial | 2603 | ||
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| Author | Isabelle Guyon; Kristin Bennett; Gavin Cawley; Hugo Jair Escalante; Sergio Escalera; Tin Kam Ho; Nuria Macia; Bisakha Ray; Alexander Statnikov; Evelyne Viegas | ||||
| Title | Design of the 2015 ChaLearn AutoML Challenge | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE International Joint Conference on Neural Networks IJCNN2015 | Abbreviated Journal | |
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| Abstract | ChaLearn is organizing for IJCNN 2015 an Automatic Machine Learning challenge (AutoML) to solve classification and regression problems from given feature representations, without any human intervention. This is a challenge with code
submission: the code submitted can be executed automatically on the challenge servers to train and test learning machines on new datasets. However, there is no obligation to submit code. Half of the prizes can be won by just submitting prediction results. There are six rounds (Prep, Novice, Intermediate, Advanced, Expert, and Master) in which datasets of progressive difficulty are introduced (5 per round). There is no requirement to participate in previous rounds to enter a new round. The rounds alternate AutoML phases in which submitted code is “blind tested” on datasets the participants have never seen before, and Tweakathon phases giving time (' 1 month) to the participants to improve their methods by tweaking their code on those datasets. This challenge will push the state-of-the-art in fully automatic machine learning on a wide range of problems taken from real world applications. The platform will remain available beyond the termination of the challenge: http://codalab.org/AutoML |
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| Address | Killarney; Ireland; July 2015 | ||||
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| Area | Expedition | Conference | IJCNN | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ GBC2015a | Serial | 2604 | ||
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| Author | Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio | ||||
| Title | A computational framework for cancer response assessment based on oncological PET-CT scans | Type | Journal Article | ||
| Year | 2014 | Publication | Computers in Biology and Medicine | Abbreviated Journal | CBM |
| Volume | 55 | Issue | Pages | 92–99 | |
| Keywords | Computer aided diagnosis; Nuclear medicine; Machine learning; Image processing; Quantitative analysis | ||||
| Abstract | In this work we present a comprehensive computational framework to help in the clinical assessment of cancer response from a pair of time consecutive oncological PET-CT scans. In this scenario, the design and implementation of a supervised machine learning system to predict and quantify cancer progression or response conditions by introducing a novel feature set that models the underlying clinical context is described. Performance results in 100 clinical cases (corresponding to 200 whole body PET-CT scans) in comparing expert-based visual analysis and classifier decision making show up to 70% accuracy within a completely automatic pipeline and 90% accuracy when providing the system with expert-guided PET tumor segmentation masks. | ||||
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| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ SED2014 | Serial | 2606 | ||
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| Author | Maedeh Aghaei; Petia Radeva | ||||
| Title | Bag-of-Tracklets for Person Tracking in Life-Logging Data | Type | Conference Article | ||
| Year | 2014 | Publication | 17th International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
| Volume | 269 | Issue | Pages | 35-44 | |
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| Abstract | 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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ISBN | 978-1-61499-451-0 | Medium | ||
| Area | Expedition | Conference | CCIA | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ AgR2015 | Serial | 2607 | ||
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| Author | Enric Marti; J.Roncaries; Debora Gil; Aura Hernandez-Sabate; Antoni Gurgui; Ferran Poveda | ||||
| Title | PBL On Line: A proposal for the organization, part-time monitoring and assessment of PBL group activities | Type | Journal | ||
| Year | 2015 | Publication | Journal of Technology and Science Education | Abbreviated Journal | JOTSE |
| Volume | 5 | Issue | 2 | Pages | 87-96 |
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| Notes | IAM; ADAS; 600.076; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ MRG2015 | Serial | 2608 | ||
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| Author | Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; J.Roncaries; Debora Gil | ||||
| Title | Automatic evaluation of practices in Moodle for Self Learning in Engineering | Type | Journal | ||
| Year | 2015 | Publication | Journal of Technology and Science Education | Abbreviated Journal | JOTSE |
| Volume | 5 | Issue | 2 | Pages | 97-106 |
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| Notes | IAM; DAG; 600.075; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ SRM2015 | Serial | 2610 | ||
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| Author | Carles Sanchez; Jorge Bernal; F. Javier Sanchez; Antoni Rosell; Marta Diez-Ferrer; Debora Gil | ||||
| Title | Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy | Type | Journal Article | ||
| Year | 2015 | Publication | International Journal of Computer Assisted Radiology and Surgery | Abbreviated Journal | IJCAR |
| Volume | 10 | Issue | 6 | Pages | 935-945 |
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| Notes | IAM; MV; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ SBS2015a | Serial | 2611 | ||
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| Author | Carles Sanchez; Debora Gil; R. Tazi; Jorge Bernal; Y. Ruiz; L. Planas; F. Javier Sanchez; Antoni Rosell | ||||
| Title | Quasi-real time digital assessment of Central Airway Obstruction | Type | Conference Article | ||
| Year | 2015 | Publication | 3rd European congress for bronchology and interventional pulmonology ECBIP2015 | Abbreviated Journal | |
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| Address | Barcelona; Spain; April 2015 | ||||
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| Area | Expedition | Conference | ECBIP | ||
| Notes | IAM; MV; 600.075 | Approved | no | ||
| Call Number | SGT2015 | Serial | 2612 | ||
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