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| Author | Santiago Segui; Oriol Pujol; Jordi Vitria | ||||
| Title | Learning to count with deep object features | Type | Conference Article | ||
| Year | 2015 | Publication | Deep Vision: Deep Learning in Computer Vision, CVPR 2015 Workshop | Abbreviated Journal  |
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| Volume | Issue | Pages | 90-96 | ||
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| Abstract | Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural
network in order to understand their underlying representation. To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training. We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene. |
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| Address | Boston; USA; June 2015 | ||||
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| 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 | CVPRW | ||
| Notes | MILAB; HuPBA; OR;MV | Approved | no | ||
| Call Number | Admin @ si @ SPV2015 | Serial | 2636 | ||
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| Author | Marc Bolaños; Maite Garolera; Petia Radeva | ||||
| Title | Active labeling application applied to food-related object recognition | Type | Conference Article | ||
| Year | 2013 | Publication | 5th International Workshop on Multimedia for Cooking & Eating Activities | Abbreviated Journal |
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| Volume | Issue | Pages | 45-50 | ||
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| Abstract | Every day, lifelogging devices, available for recording different aspects of our daily life, increase in number, quality and functions, just like the multiple applications that we give to them. Applying wearable devices to analyse the nutritional habits of people is a challenging application based on acquiring and analyzing life records in long periods of time. However, to extract the information of interest related to the eating patterns of people, we need automatic methods to process large amount of life-logging data (e.g. recognition of food-related objects). Creating a rich set of manually labeled samples to train the algorithms is slow, tedious and subjective. To address this problem, we propose a novel method in the framework of Active Labeling for construct- ing a training set of thousands of images. Inspired by the hierarchical sampling method for active learning [6], we propose an Active forest that organizes hierarchically the data for easy and fast labeling. Moreover, introducing a classifier into the hierarchical structures, as well as transforming the feature space for better data clustering, additionally im- prove the algorithm. Our method is successfully tested to label 89.700 food-related objects and achieves significant reduction in expert time labelling.
Active labeling application applied to food-related object recognition ResearchGate. Available from: http://www.researchgate.net/publication/262252017Activelabelingapplicationappliedtofood-relatedobjectrecognition [accessed Jul 14, 2015]. |
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| Address | Barcelona; October 2013 | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ACM-CEA | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ BGR2013b | Serial | 2637 | ||
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| Author | Marc Bolaños; R. Mestre; Estefania Talavera; Xavier Giro; Petia Radeva | ||||
| Title | Visual Summary of Egocentric Photostreams by Representative Keyframes | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE International Conference on Multimedia and Expo ICMEW2015 | Abbreviated Journal |
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| Volume | Issue | Pages | 1-6 | ||
| Keywords | egocentric; lifelogging; summarization; keyframes | ||||
| Abstract | Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e.g. memory reinforcement). In this paper, we propose a new summarization method based on keyframes selection that uses visual features extracted bymeans of a convolutional neural network. Our method applies an unsupervised clustering for dividing the photostreams into events, and finally extracts the most relevant keyframe for each event. We assess the results by applying a blind-taste test on a group of 20 people who assessed the quality of the
summaries. |
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| Address | Torino; italy; July 2015 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | 978-1-4799-7079-7 | Edition | ||
| ISSN | ISBN | 978-1-4799-7079-7 | Medium | ||
| Area | Expedition | Conference | ICME | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ BMT2015 | Serial | 2638 | ||
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| Author | Nuria Cirera; Alicia Fornes; Josep Llados | ||||
| Title | Hidden Markov model topology optimization for handwriting recognition | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal |
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| Volume | Issue | Pages | 626-630 | ||
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| Abstract | In this paper we present a method to optimize the topology of linear left-to-right hidden Markov models. These models are very popular for sequential signals modeling on tasks such as handwriting recognition. Many topology definition methods select the number of states for a character model based
on character length. This can be a drawback when characters are shorter than the minimum allowed by the model, since they can not be properly trained nor recognized. The proposed method optimizes the number of states per model by automatically including convenient skip-state transitions and therefore it avoids the aforementioned problem.We discuss and compare our method with other character length-based methods such the Fixed, Bakis and Quantile methods. Our proposal performs well on off-line handwriting recognition task. |
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| Address | Nancy; France; August 2015 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ CFL2015 | Serial | 2639 | ||
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| Author | Juan Ignacio Toledo; Jordi Cucurull; Jordi Puiggali; Alicia Fornes; Josep Llados | ||||
| Title | Document Analysis Techniques for Automatic Electoral Document Processing: A Survey | Type | Conference Article | ||
| Year | 2015 | Publication | E-Voting and Identity, Proceedings of 5th international conference, VoteID 2015 | Abbreviated Journal |
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| Volume | Issue | Pages | 139-141 | ||
| Keywords | Document image analysis; Computer vision; Paper ballots; Paper based elections; Optical scan; Tally | ||||
| Abstract | In this paper, we will discuss the most common challenges in electoral document processing and study the different solutions from the document analysis community that can be applied in each case. We will cover Optical Mark Recognition techniques to detect voter selections in the Australian Ballot, handwritten number recognition for preferential elections and handwriting recognition for write-in areas. We will also propose some particular adjustments that can be made to those general techniques in the specific context of electoral documents. | ||||
| Address | Bern; Switzerland; September 2015 | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | VoteID | ||
| Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ TCP2015 | Serial | 2641 | ||
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| Author | Pau Riba; Josep Llados; Alicia Fornes | ||||
| Title | Handwritten Word Spotting by Inexact Matching of Grapheme Graphs | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal |
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| Volume | Issue | Pages | 781 - 785 | ||
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| Abstract | This paper presents a graph-based word spotting for handwritten documents. Contrary to most word spotting techniques, which use statistical representations, we propose a structural representation suitable to be robust to the inherent deformations of handwriting. Attributed graphs are constructed using a part-based approach. Graphemes extracted from shape convexities are used as stable units of handwriting, and are associated to graph nodes. Then, spatial relations between them determine graph edges. Spotting is defined in terms of an error-tolerant graph matching using bipartite-graph matching algorithm. To make the method usable in large datasets, a graph indexing approach that makes use of binary embeddings is used as preprocessing. Historical documents are used as experimental framework. The approach is comparable to statistical ones in terms of time and memory requirements, especially when dealing with large document collections. | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.077; 600.061; 602.006 | Approved | no | ||
| Call Number | Admin @ si @ RLF2015b | Serial | 2642 | ||
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| Author | Onur Ferhat; Arcadi Llanza; Fernando Vilariño | ||||
| Title | A Feature-Based Gaze Estimation Algorithm for Natural Light Scenarios | Type | Conference Article | ||
| Year | 2015 | Publication | Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 | Abbreviated Journal |
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| Volume | 9117 | Issue | Pages | 569-576 | |
| Keywords | Eye tracking; Gaze estimation; Natural light; Webcam | ||||
| Abstract | We present an eye tracking system that works with regular webcams. We base our work on open source CVC Eye Tracker [7] and we propose a number of improvements and a novel gaze estimation method. The new method uses features extracted from iris segmentation and it does not fall into the traditional categorization of appearance–based/model–based methods. Our experiments show that our approach reduces the gaze estimation errors by 34 % in the horizontal direction and by 12 % in the vertical direction compared to the baseline system. | ||||
| Address | Santiago de Compostela; June 2015 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer International Publishing | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-319-19389-2 | Medium | |
| Area | Expedition | Conference | IbPRIA | ||
| Notes | MV;SIAI | Approved | no | ||
| Call Number | Admin @ si @ FLV2015a | Serial | 2646 | ||
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| Author | Kamal Nasrollahi; Sergio Escalera; P. Rasti; Gholamreza Anbarjafari; Xavier Baro; Hugo Jair Escalante; Thomas B. Moeslund | ||||
| Title | Deep Learning based Super-Resolution for Improved Action Recognition | Type | Conference Article | ||
| Year | 2015 | Publication | 5th International Conference on Image Processing Theory, Tools and Applications IPTA2015 | Abbreviated Journal |
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| Volume | Issue | Pages | 67 - 72 | ||
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| Abstract | Action recognition systems mostly work with videos of proper quality and resolution. Even most challenging benchmark databases for action recognition, hardly include videos of low-resolution from, e.g., surveillance cameras. In videos recorded by such cameras, due to the distance between people and cameras, people are pictured very small and hence challenge action recognition algorithms. Simple upsampling methods, like bicubic interpolation, cannot retrieve all the detailed information that can help the recognition. To deal with this problem, in this paper we combine results of bicubic interpolation with results of a state-ofthe-art deep learning-based super-resolution algorithm, through an alpha-blending approach. The experimental results obtained on down-sampled version of a large subset of Hoolywood2 benchmark database show the importance of the proposed system in increasing the recognition rate of a state-of-the-art action recognition system for handling low-resolution videos. | ||||
| Address | Orleans; France; November 2015 | ||||
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| Area | Expedition | Conference | IPTA | ||
| Notes | HuPBA;MV | Approved | no | ||
| Call Number | Admin @ si @ NER2015 | Serial | 2648 | ||
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| Author | Isabelle Guyon; Kristin Bennett; Gavin Cawley; Hugo Jair Escalante; Sergio Escalera | ||||
| Title | The AutoML challenge on codalab | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE International Joint Conference on Neural Networks IJCNN2015 | Abbreviated Journal |
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| Address | Killarney; Ireland; July 2015 | ||||
| Corporate Author | Thesis | ||||
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| Area | Expedition | Conference | IJCNN | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ GBC2015b | Serial | 2650 | ||
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| Author | Gerard Canal; Cecilio Angulo; Sergio Escalera | ||||
| Title | Gesture based Human Multi-Robot interaction | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE International Joint Conference on Neural Networks IJCNN2015 | Abbreviated Journal |
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| Abstract | The emergence of robot applications for nontechnical users implies designing new ways of interaction between robotic platforms and users. The main goal of this work is the development of a gestural interface to interact with robots
in a similar way as humans do, allowing the user to provide information of the task with non-verbal communication. The gesture recognition application has been implemented using the Microsoft’s KinectTM v2 sensor. Hence, a real-time algorithm based on skeletal features is described to deal with both, static gestures and dynamic ones, being the latter recognized using a weighted Dynamic Time Warping method. The gesture recognition application has been implemented in a multi-robot case. A NAO humanoid robot is in charge of interacting with the users and respond to the visual signals they produce. Moreover, a wheeled Wifibot robot carries both the sensor and the NAO robot, easing navigation when necessary. A broad set of user tests have been carried out demonstrating that the system is, indeed, a natural approach to human robot interaction, with a fast response and easy to use, showing high gesture recognition rates. |
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| Address | Killarney; Ireland; July 2015 | ||||
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| Area | Expedition | Conference | IJCNN | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | CAE2015a | Serial | 2651 | ||
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| Author | Xavier Baro; Jordi Gonzalez; Junior Fabian; Miguel Angel Bautista; Marc Oliu; Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera | ||||
| Title | ChaLearn Looking at People 2015 challenges: action spotting and cultural event recognition | Type | Conference Article | ||
| Year | 2015 | Publication | 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) | Abbreviated Journal |
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| Volume | Issue | Pages | 1-9 | ||
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| Abstract | Following previous series on Looking at People (LAP) challenges [6, 5, 4], ChaLearn ran two competitions to be presented at CVPR 2015: action/interaction spotting and cultural event recognition in RGB data. We ran a second round on human activity recognition on RGB data sequences. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes the two performed challenges and obtained results. Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/. | ||||
| Address | Boston; EEUU; June 2015 | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | HuPBA;MV | Approved | no | ||
| Call Number | Serial | 2652 | |||
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| Author | Andres Traumann; Sergio Escalera; Gholamreza Anbarjafari | ||||
| Title | A New Retexturing Method for Virtual Fitting Room Using Kinect 2 Camera | Type | Conference Article | ||
| Year | 2015 | Publication | 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) | Abbreviated Journal |
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| Volume | Issue | Pages | 75-79 | ||
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| Address | Boston; EEUU; June 2015 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ TEA2015 | Serial | 2653 | ||
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| Author | Ramin Irani; Kamal Nasrollahi; Chris Bahnsen; D.H. Lundtoft; Thomas B. Moeslund; Marc O. Simon; Ciprian Corneanu; Sergio Escalera; Tanja L. Pedersen; Maria-Louise Klitgaard; Laura Petrini | ||||
| Title | Spatio-temporal Analysis of RGB-D-T Facial Images for Multimodal Pain Level Recognition | Type | Conference Article | ||
| Year | 2015 | Publication | 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) | Abbreviated Journal |
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| Volume | Issue | Pages | 88-95 | ||
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| Abstract | Pain is a vital sign of human health and its automatic detection can be of crucial importance in many different contexts, including medical scenarios. While most available computer vision techniques are based on RGB, in this paper, we investigate the effect of combining RGB, depth, and thermal
facial images for pain detection and pain intensity level recognition. For this purpose, we extract energies released by facial pixels using a spatiotemporal filter. Experiments on a group of 12 elderly people applying the multimodal approach show that the proposed method successfully detects pain and recognizes between three intensity levels in 82% of the analyzed frames improving more than 6% over RGB only analysis in similar conditions. |
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| Address | Boston; EEUU; June 2015 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ INB2015 | Serial | 2654 | ||
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| Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Albert Clapes; Kamal Nasrollahi; Michael Holte; Thomas B. Moeslund | ||||
| Title | Keep it Accurate and Diverse: Enhancing Action Recognition Performance by Ensemble Learning | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) | Abbreviated Journal |
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| Volume | Issue | Pages | 22-29 | ||
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| Abstract | The performance of different action recognition techniques has recently been studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of action learning techniques, each performing the recognition task from a different perspective.
The underlying idea is that instead of aiming a very sophisticated and powerful representation/learning technique, we can learn action categories using a set of relatively simple and diverse classifiers, each trained with different feature set. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selection of a learner on an unseen action recognition scenario. This leads to having a more robust and general-applicable framework. In order to improve the recognition performance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use of diversity of base learners trained on different sources of information. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing enhanced performance of the proposed methodology. |
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| Address | Boston; EEUU; June 2015 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ BGE2015 | Serial | 2655 | ||
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| Author | Isabelle Guyon; Kristin Bennett; Gavin Cawley; Hugo Jair Escalante; Sergio Escalera; Tin Kam Ho; Nuria Macia; Bisakha Ray; Mehreen Saeed; Alexander Statnikov; Evelyne Viegas | ||||
| Title | AutoML Challenge 2015: Design and First Results | Type | Conference Article | ||
| Year | 2015 | Publication | 32nd International Conference on Machine Learning, ICML workshop, JMLR proceedings ICML15 | Abbreviated Journal |
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| Volume | Issue | Pages | 1-8 | ||
| Keywords | AutoML Challenge; machine learning; model selection; meta-learning; repre- sentation learning; active learning | ||||
| Abstract | ChaLearn is organizing the Automatic Machine Learning (AutoML) contest 2015, which challenges participants to solve classi cation and regression problems without any human intervention. Participants' code is automatically run on the contest servers to train and test learning machines. However, there is no obligation to submit code; half of the prizes can be won by submitting prediction results only. Datasets of progressively increasing diculty are introduced throughout the six rounds of the challenge. (Participants can
enter the competition in any round.) The rounds alternate phases in which learners are tested on datasets participants have not seen (AutoML), and phases in which participants have limited time to tweak their algorithms on those datasets to improve performance (Tweakathon). This challenge will push the state of the art in fully automatic machine learning on a wide range of real-world problems. The platform will remain available beyond the termination of the challenge: http://codalab.org/AutoML. |
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| Address | Lille; France; July 2015 | ||||
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| Area | Expedition | Conference | ICML | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ GBC2015c | Serial | 2656 | ||
| Permanent link to this record | |||||