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| Author | Fahad Shahbaz Khan; Jiaolong Xu; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez | ||||
| Title | Recognizing Actions through Action-specific Person Detection | Type | Journal Article | ||
| Year | 2015 | Publication  |
IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 24 | Issue | 11 | Pages | 4422-4432 |
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| Abstract | Action recognition in still images is a challenging problem in computer vision. To facilitate comparative evaluation independently of person detection, the standard evaluation protocol for action recognition uses an oracle person detector to obtain perfect bounding box information at both training and test time. The assumption is that, in practice, a general person detector will provide candidate bounding boxes for action recognition. In this paper, we argue that this paradigm is suboptimal and that action class labels should already be considered during the detection stage. Motivated by the observation that body pose is strongly conditioned on action class, we show that: 1) the existing state-of-the-art generic person detectors are not adequate for proposing candidate bounding boxes for action classification; 2) due to limited training examples, the direct training of action-specific person detectors is also inadequate; and 3) using only a small number of labeled action examples, the transfer learning is able to adapt an existing detector to propose higher quality bounding boxes for subsequent action classification. To the best of our knowledge, we are the first to investigate transfer learning for the task of action-specific person detection in still images. We perform extensive experiments on two benchmark data sets: 1) Stanford-40 and 2) PASCAL VOC 2012. For the action detection task (i.e., both person localization and classification of the action performed), our approach outperforms methods based on general person detection by 5.7% mean average precision (MAP) on Stanford-40 and 2.1% MAP on PASCAL VOC 2012. Our approach also significantly outperforms the state of the art with a MAP of 45.4% on Stanford-40 and 31.4% on PASCAL VOC 2012. We also evaluate our action detection approach for the task of action classification (i.e., recognizing actions without localizing them). For this task, our approach, without using any ground-truth person localization at test tim- , outperforms on both data sets state-of-the-art methods, which do use person locations. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1057-7149 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; LAMP; 600.076; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ KXR2015 | Serial | 2668 | ||
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| Author | Lluis Garrido; M.Guerrieri; Laura Igual | ||||
| Title | Image Segmentation with Cage Active Contours | Type | Journal Article | ||
| Year | 2015 | Publication |
IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 24 | Issue | 12 | Pages | 5557 - 5566 |
| Keywords | Level sets; Mean value coordinates; Parametrized active contours; level sets; mean value coordinates | ||||
| Abstract | In this paper, we present a framework for image segmentation based on parametrized active contours. The evolving contour is parametrized according to a reduced set of control points that form a closed polygon and have a clear visual interpretation. The parametrization, called mean value coordinates, stems from the techniques used in computer graphics to animate virtual models. Our framework allows to easily formulate region-based energies to segment an image. In particular, we present three different local region-based energy terms: 1) the mean model; 2) the Gaussian model; 3) and the histogram model. We show the behavior of our method on synthetic and real images and compare the performance with state-of-the-art level set methods. | ||||
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| ISSN | 1057-7149 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ GGI2015 | Serial | 2673 | ||
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| Author | Mikhail Mozerov; Joost Van de Weijer | ||||
| Title | Global Color Sparseness and a Local Statistics Prior for Fast Bilateral Filtering | Type | Journal Article | ||
| Year | 2015 | Publication |
IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 24 | Issue | 12 | Pages | 5842-5853 |
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| Abstract | The property of smoothing while preserving edges makes the bilateral filter a very popular image processing tool. However, its non-linear nature results in a computationally costly operation. Various works propose fast approximations to the bilateral filter. However, the majority does not generalize to vector input as is the case with color images. We propose a fast approximation to the bilateral filter for color images. The filter is based on two ideas. First, the number of colors, which occur in a single natural image, is limited. We exploit this color sparseness to rewrite the initial non-linear bilateral filter as a number of linear filter operations. Second, we impose a statistical prior to the image values that are locally present within the filter window. We show that this statistical prior leads to a closed-form solution of the bilateral filter. Finally, we combine both ideas into a single fast and accurate bilateral filter for color images. Experimental results show that our bilateral filter based on the local prior yields an extremely fast bilateral filter approximation, but with limited accuracy, which has potential application in real-time video filtering. Our bilateral filter, which combines color sparseness and local statistics, yields a fast and accurate bilateral filter approximation and obtains the state-of-the-art results. | ||||
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| ISSN | 1057-7149 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP; 600.079;ISE | Approved | no | ||
| Call Number | Admin @ si @ MoW2015b | Serial | 2689 | ||
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| Author | Sergio Escalera; Jordi Gonzalez; Xavier Baro; Pablo Pardo; Junior Fabian; Marc Oliu; Hugo Jair Escalante; Ivan Huerta; Isabelle Guyon | ||||
| Title | ChaLearn Looking at People 2015 new competitions: Age Estimation and Cultural Event Recognition | Type | Conference Article | ||
| Year | 2015 | Publication |
IEEE International Joint Conference on Neural Networks IJCNN2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 1-8 | ||
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| Abstract | Following previous series on Looking at People (LAP) challenges [1], [2], [3], in 2015 ChaLearn runs two new competitions within the field of Looking at People: age and cultural event recognition in still images. We propose thefirst crowdsourcing application to collect and label data about apparent
age of people instead of the real age. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes both challenges and data, providing some initial baselines. The results of the first round of the competition were presented at ChaLearn LAP 2015 IJCNN special session on computer vision and robotics http://www.dtic.ua.es/∼jgarcia/IJCNN2015. Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/. |
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| Address | Killarney; Ireland; July 2015 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | IJCNN | ||
| Notes | HuPBA; ISE; 600.063; 600.078;MV | Approved | no | ||
| Call Number | Admin @ si @ EGB2015 | Serial | 2591 | ||
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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 | 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 | ||||
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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 | M. Campos-Taberner; Adriana Romero; Carlo Gatta; Gustavo Camps-Valls | ||||
| Title | Shared feature representations of LiDAR and optical images: Trading sparsity for semantic discrimination | Type | Conference Article | ||
| Year | 2015 | Publication |
IEEE International Geoscience and Remote Sensing Symposium IGARSS2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 4169 - 4172 | ||
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| Abstract | This paper studies the level of complementary information conveyed by extremely high resolution LiDAR and optical images. We pursue this goal following an indirect approach via unsupervised spatial-spectral feature extraction. We used a recently presented unsupervised convolutional neural network trained to enforce both population and lifetime spar-sity in the feature representation. We derived independent and joint feature representations, and analyzed the sparsity scores and the discriminative power. Interestingly, the obtained results revealed that the RGB+LiDAR representation is no longer sparse, and the derived basis functions merge color and elevation yielding a set of more expressive colored edge filters. The joint feature representation is also more discriminative when used for clustering and topological data visualization. | ||||
| Address | Milan; Italy; July 2015 | ||||
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| Area | Expedition | Conference | IGARSS | ||
| Notes | LAMP; 600.079;MILAB | Approved | no | ||
| Call Number | Admin @ si @ CRG2015 | Serial | 2724 | ||
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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 | |
| 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 | ||||
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| 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 | Alejandro Gonzalez Alzate; Gabriel Villalonga; Jiaolong Xu; David Vazquez; Jaume Amores; Antonio Lopez | ||||
| Title | Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection | Type | Conference Article | ||
| Year | 2015 | Publication |
IEEE Intelligent Vehicles Symposium IV2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 356-361 | ||
| Keywords | Pedestrian Detection | ||||
| Abstract | Despite recent significant advances, pedestrian detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities and a strong multi-view classifier that accounts for different pedestrian views and poses. In this paper we provide an extensive evaluation that gives insight into how each of these aspects (multi-cue, multimodality and strong multi-view classifier) affect performance both individually and when integrated together. In the multimodality component we explore the fusion of RGB and depth maps obtained by high-definition LIDAR, a type of modality that is only recently starting to receive attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the performance, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. These simple blocks can be easily replaced with more sophisticated ones recently proposed, such as the use of convolutional neural networks for feature representation, to further improve the accuracy. | ||||
| Address | Seoul; Corea; June 2015 | ||||
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| Area | ACDC | Expedition | Conference | IV | |
| Notes | ADAS; 600.076; 600.057; 600.054 | Approved | no | ||
| Call Number | ADAS @ adas @ GVX2015 | Serial | 2625 | ||
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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 | |
| 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 | G.Thorvaldsen; Joana Maria Pujadas-Mora; T.Andersen ; L.Eikvil; Josep Llados; Alicia Fornes; Anna Cabre | ||||
| Title | A Tale of two Transcriptions | Type | Journal | ||
| Year | 2015 | Publication |
Historical Life Course Studies | Abbreviated Journal | |
| Volume | 2 | Issue | Pages | 1-19 | |
| Keywords | Nominative Sources; Census; Vital Records; Computer Vision; Optical Character Recognition; Word Spotting | ||||
| Abstract | non-indexed
This article explains how two projects implement semi-automated transcription routines: for census sheets in Norway and marriage protocols from Barcelona. The Spanish system was created to transcribe the marriage license books from 1451 to 1905 for the Barcelona area; one of the world’s longest series of preserved vital records. Thus, in the Project “Five Centuries of Marriages” (5CofM) at the Autonomous University of Barcelona’s Center for Demographic Studies, the Barcelona Historical Marriage Database has been built. More than 600,000 records were transcribed by 150 transcribers working online. The Norwegian material is cross-sectional as it is the 1891 census, recorded on one sheet per person. This format and the underlining of keywords for several variables made it more feasible to semi-automate data entry than when many persons are listed on the same page. While Optical Character Recognition (OCR) for printed text is scientifically mature, computer vision research is now focused on more difficult problems such as handwriting recognition. In the marriage project, document analysis methods have been proposed to automatically recognize the marriage licenses. Fully automatic recognition is still a challenge, but some promising results have been obtained. In Spain, Norway and elsewhere the source material is available as scanned pictures on the Internet, opening up the possibility for further international cooperation concerning automating the transcription of historic source materials. Like what is being done in projects to digitize printed materials, the optimal solution is likely to be a combination of manual transcription and machine-assisted recognition also for hand-written sources. |
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| ISSN | 2352-6343 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.077; 602.006 | Approved | no | ||
| Call Number | Admin @ si @ TPA2015 | Serial | 2582 | ||
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| Author | Julie Digne; Mariella Dimiccoli; Neus Sabater; Philippe Salembier | ||||
| Title | Neighborhood Filters and the Recovery of 3D Information | Type | Book Chapter | ||
| Year | 2015 | Publication |
Handbook of Mathematical Methods in Imaging | Abbreviated Journal | |
| Volume | Issue | III | Pages | 1645-1673 | |
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| Abstract | Following their success in image processing (see Chapter Local Smoothing Neighborhood Filters), neighborhood filters have been extended to 3D surface processing. This adaptation is not straightforward. It has led to several variants for surfaces depending on whether the surface is defined as a mesh, or as a raw data point set. The image gray level in the bilateral similarity measure is replaced by a geometric information such as the normal or the curvature. The first section of this chapter reviews the variants of 3D mesh bilateral filters and compares them to the simplest possible isotropic filter, the mean curvature motion.In a second part, this chapter reviews applications of the bilateral filter to a data composed of a sparse depth map (or of depth cues) and of the image on which they have been computed. Such sparse depth cues can be obtained by stereovision or by psychophysical techniques. The underlying assumption to these applications is that pixels with similar intensity around a region are likely to have similar depths. Therefore, when diffusing depth information with a bilateral filter based on locality and color similarity, the discontinuities in depth are assured to be consistent with the color discontinuities, which is generally a desirable property. In the reviewed applications, this ends up with the reconstruction of a dense perceptual depth map from the joint data of an image and of depth cues. | ||||
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| Publisher | Springer New York | Place of Publication | Editor | ||
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| ISSN | ISBN | 978-1-4939-0789-2 | Medium | ||
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ DDS2015 | Serial | 2710 | ||
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| Author | Olivier Penacchio; Xavier Otazu; A. wilkins; J. Harris | ||||
| Title | Uncomfortable images prevent lateral interactions in the cortex from providing a sparse code | Type | Conference Article | ||
| Year | 2015 | Publication |
European Conference on Visual Perception ECVP2015 | Abbreviated Journal | |
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| Address | Liverpool; uk; August 2015 | ||||
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| Area | Expedition | Conference | ECVP | ||
| Notes | NEUROBIT; | Approved | no | ||
| Call Number | Admin @ si @ POW2015 | Serial | 2633 | ||
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