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Author |
Adriana Romero; Petia Radeva; Carlo Gatta |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Meta-parameter free unsupervised sparse feature learning |
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Journal Article |
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Year |
2015 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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37 |
Issue |
8 |
Pages |
1716-1722 |
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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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MILAB; 600.068; 600.079; 601.160 |
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no |
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Admin @ si @ RRG2014b |
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2594 |
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Author |
Christophe Rigaud; Clement Guerin; Dimosthenis Karatzas; Jean-Christophe Burie; Jean-Marc Ogier |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Knowledge-driven understanding of images in comic books |
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Journal Article |
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Year |
2015 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
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Volume |
18 |
Issue |
3 |
Pages |
199-221 |
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Keywords |
Document Understanding; comics analysis; expert system |
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Document analysis is an active field of research, which can attain a complete understanding of the semantics of a given document. One example of the document understanding process is enabling a computer to identify the key elements of a comic book story and arrange them according to a predefined domain knowledge. In this study, we propose a knowledge-driven system that can interact with bottom-up and top-down information to progressively understand the content of a document. We model the comic book’s and the image processing domains knowledge for information consistency analysis. In addition, different image processing methods are improved or developed to extract panels, balloons, tails, texts, comic characters and their semantic relations in an unsupervised way. |
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Springer Berlin Heidelberg |
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1433-2833 |
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DAG; 600.056; 600.077 |
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RGK2015 |
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2595 |
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Author |
Marc Bolaños; Maite Garolera; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Object Discovery using CNN Features in Egocentric Videos |
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Conference Article |
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Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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9117 |
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67-74 |
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Object discovery; Egocentric videos; Lifelogging; CNN |
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Lifelogging devices based on photo/video are spreading faster everyday. This growth can represent great benefits to develop methods for extraction of meaningful information about the user wearing the device and his/her environment. In this paper, we propose a semi-supervised strategy for easily discovering objects relevant to the person wearing a first-person camera. The egocentric video sequence acquired by the camera, uses both the appearance extracted by means of a deep convolutional neural network and an object refill methodology that allow to discover objects even in case of small amount of object appearance in the collection of images. We validate our method on a sequence of 1000 egocentric daily images and obtain results with an F-measure of 0.5, 0.17 better than the state of the art approach. |
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Santiago de Compostela; España; June 2015 |
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0302-9743 |
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978-3-319-19389-2 |
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IbPRIA |
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MILAB |
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no |
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Admin @ si @ BGR2015 |
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2596 |
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Author |
Estefania Talavera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
R-clustering for egocentric video segmentation |
Type |
Conference Article |
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Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
Abbreviated Journal |
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Volume |
9117 |
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Pages |
327-336 |
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Keywords |
Temporal video segmentation; Egocentric videos; Clustering |
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In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energy-minimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate both techniques in an energy-minimization framework that serves to disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames descriptors. We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods. |
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Santiago de Compostela; España; June 2015 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-19389-2 |
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IbPRIA |
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MILAB |
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no |
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Admin @ si @ TDB2015 |
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2597 |
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Author |
Manuel Graña; Bogdan Raducanu |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Special Issue on Bioinspired and knowledge based techniques and applications |
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Journal Article |
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2015 |
Publication |
Neurocomputing |
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NEUCOM |
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1-3 |
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LAMP; |
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no |
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Admin @ si @ GrR2015 |
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2598 |
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Author |
Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika |
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Title |
Facial Expression Recognition based on Multi-view Observations with Application to Social Robotics |
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Conference Article |
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Year |
2014 |
Publication |
1st Workshop on Computer Vision for Affective Computing |
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1-8 |
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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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Singapore; November 2014 |
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ACCV |
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LAMP; |
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no |
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Admin @ si @ RBD2014 |
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2599 |
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Author |
C. Alejandro Parraga |
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Perceptual Psychophysics |
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Book Chapter |
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Year |
2015 |
Publication |
Biologically-Inspired Computer Vision: Fundamentals and Applications |
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G.Cristobal; M.Keil; L.Perrinet |
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978-3-527-41264-8 |
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CIC; 600.074 |
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no |
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Admin @ si @ Par2015 |
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2600 |
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Author |
Firat Ismailoglu; Ida G. Sprinkhuizen-Kuyper; Evgueni Smirnov; Sergio Escalera; Ralf Peeters |
![goto web page url](img/www.gif)
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Title |
Fractional Programming Weighted Decoding for Error-Correcting Output Codes |
Type |
Conference Article |
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Year |
2015 |
Publication |
Multiple Classifier Systems, Proceedings of 12th International Workshop , MCS 2015 |
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38-50 |
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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. |
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Gunzburg; Germany; June 2015 |
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Springer International Publishing |
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978-3-319-20247-1 |
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MCS |
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HuPBA;MILAB |
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no |
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Admin @ si @ ISS2015 |
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2601 |
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Author |
Hugo Jair Escalante; Jose Martinez; Sergio Escalera; Victor Ponce; Xavier Baro |
![goto web page url](img/www.gif)
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Title |
Improving Bag of Visual Words Representations with Genetic Programming |
Type |
Conference Article |
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Year |
2015 |
Publication |
IEEE International Joint Conference on Neural Networks IJCNN2015 |
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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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Killarney; Ireland; July 2015 |
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IJCNN |
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HuPBA;MV |
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no |
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Admin @ si @ EME2015 |
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2603 |
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Isabelle Guyon; Kristin Bennett; Gavin Cawley; Hugo Jair Escalante; Sergio Escalera; Tin Kam Ho; Nuria Macia; Bisakha Ray; Alexander Statnikov; Evelyne Viegas |
![goto web page url](img/www.gif)
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Title |
Design of the 2015 ChaLearn AutoML Challenge |
Type |
Conference Article |
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Year |
2015 |
Publication |
IEEE International Joint Conference on Neural Networks IJCNN2015 |
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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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Killarney; Ireland; July 2015 |
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IJCNN |
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HuPBA;MILAB |
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no |
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Admin @ si @ GBC2015a |
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2604 |
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Author |
Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
A computational framework for cancer response assessment based on oncological PET-CT scans |
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Journal Article |
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2014 |
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Computers in Biology and Medicine |
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CBM |
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55 |
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92–99 |
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Computer aided diagnosis; Nuclear medicine; Machine learning; Image processing; Quantitative analysis |
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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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HuPBA;MILAB |
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no |
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Admin @ si @ SED2014 |
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2606 |
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Author |
Maedeh Aghaei; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Bag-of-Tracklets for Person Tracking in Life-Logging Data |
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Conference Article |
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2014 |
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17th International Conference of the Catalan Association for Artificial Intelligence |
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269 |
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35-44 |
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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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978-1-61499-451-0 |
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CCIA |
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MILAB |
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no |
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Admin @ si @ AgR2015 |
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2607 |
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Author |
Enric Marti; J.Roncaries; Debora Gil; Aura Hernandez-Sabate; Antoni Gurgui; Ferran Poveda |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
PBL On Line: A proposal for the organization, part-time monitoring and assessment of PBL group activities |
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Journal |
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2015 |
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Journal of Technology and Science Education |
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JOTSE |
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5 |
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2 |
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87-96 |
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Admin @ si @ MRG2015 |
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2608 |
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Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Debora Gil; Cristina Rodriguez de Miguel; Fernando Vilariño |
![download PDF file pdf](img/file_PDF.gif)
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WM-DOVA Maps for Accurate Polyp Highlighting in Colonoscopy: Validation vs. Saliency Maps from Physicians |
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2015 |
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Computerized Medical Imaging and Graphics |
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CMIG |
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43 |
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99-111 |
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Polyp localization; Energy Maps; Colonoscopy; Saliency; Valley detection |
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We introduce in this paper a novel polyp localization method for colonoscopy videos. Our method is based on a model of appearance for polyps which defines polyp boundaries in terms of valley information. We propose the integration of valley information in a robust way fostering complete, concave and continuous boundaries typically associated to polyps. This integration is done by using a window of radial sectors which accumulate valley information to create WMDOVA1 energy maps related with the likelihood of polyp presence. We perform a double validation of our maps, which include the introduction of two new databases, including the first, up to our knowledge, fully annotated database with clinical metadata associated. First we assess that the highest value corresponds with the location of the polyp in the image. Second, we show that WM-DOVA energy maps can be comparable with saliency maps obtained from physicians' fixations obtained via an eye-tracker. Finally, we prove that our method outperforms state-of-the-art computational saliency results. Our method shows good performance, particularly for small polyps which are reported to be the main sources of polyp miss-rate, which indicates the potential applicability of our method in clinical practice. |
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0895-6111 |
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MV; IAM; 600.047; 600.060; 600.075;SIAI |
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Admin @ si @ BSF2015 |
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2609 |
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Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; J.Roncaries; Debora Gil |
![goto web page (via DOI) doi](img/doi.gif)
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Automatic evaluation of practices in Moodle for Self Learning in Engineering |
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2015 |
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Journal of Technology and Science Education |
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JOTSE |
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5 |
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2 |
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97-106 |
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IAM; DAG; 600.075; 600.077 |
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Admin @ si @ SRM2015 |
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2610 |
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