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| Author | Adria Ruiz; Joost Van de Weijer; Xavier Binefa | ||||
| Title | Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization | Type | Conference Article | ||
| Year | 2014 | Publication  |
25th British Machine Vision Conference | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | We address the problem of estimating high-level semantic labels for videos of recorded people by means of analysing their facial expressions. This problem, to which we refer as facial behavior categorization, is a weakly-supervised learning problem where we do not have access to frame-by-frame facial gesture annotations but only weak-labels at the video level are available. Therefore, the goal is to learn a set of discriminative expressions and how they determine the video weak-labels. Facial behavior categorization can be posed as a Multi-Instance-Learning (MIL) problem and we propose a novel MIL method called Regularized Multi-Concept MIL to solve it. In contrast to previous approaches applied in facial behavior analysis, RMC-MIL follows a Multi-Concept assumption which allows different facial expressions (concepts) to contribute differently to the video-label. Moreover, to handle with the high-dimensional nature of facial-descriptors, RMC-MIL uses a discriminative approach to model the concepts and structured sparsity regularization to discard non-informative features. RMC-MIL is posed as a convex-constrained optimization problem where all the parameters are jointly learned using the Projected-Quasi-Newton method. In our experiments, we use two public data-sets to show the advantages of the Regularized Multi-Concept approach and its improvement compared to existing MIL methods. RMC-MIL outperforms state-of-the-art results in the UNBC data-set for pain detection. | ||||
| Address | Nottingham; UK; September 2014 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | BMVC | ||
| Notes | LAMP; CIC; 600.074; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ RWB2014 | Serial | 2508 | ||
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| Author | Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez | ||||
| Title | Incremental Domain Adaptation of Deformable Part-based Models | Type | Conference Article | ||
| Year | 2014 | Publication |
25th British Machine Vision Conference | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Pedestrian Detection; Part-based models; Domain Adaptation | ||||
| Abstract | Nowadays, classifiers play a core role in many computer vision tasks. The underlying assumption for learning classifiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classifiers. However, in practice, there are different reasons that can break this constancy assumption. Accordingly, reusing existing classifiers by adapting them from the previous training environment (source domain) to the new testing one (target domain)
is an approach with increasing acceptance in the computer vision community. In this paper we focus on the domain adaptation of deformable part-based models (DPMs) for object detection. In particular, we focus on a relatively unexplored scenario, i.e. incremental domain adaptation for object detection assuming weak-labeling. Therefore, our algorithm is ready to improve existing source-oriented DPM-based detectors as soon as a little amount of labeled target-domain training data is available, and keeps improving as more of such data arrives in a continuous fashion. For achieving this, we follow a multiple instance learning (MIL) paradigm that operates in an incremental per-image basis. As proof of concept, we address the challenging scenario of adapting a DPM-based pedestrian detector trained with synthetic pedestrians to operate in real-world scenarios. The obtained results show that our incremental adaptive models obtain equally good accuracy results as the batch learned models, while being more flexible for handling continuously arriving target-domain data. |
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| Address | Nottingham; uk; September 2014 | ||||
| Corporate Author | Thesis | ||||
| Publisher | BMVA Press | Place of Publication | Editor | Valstar, Michel and French, Andrew and Pridmore, Tony | |
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| Area | Expedition | Conference | BMVC | ||
| Notes | ADAS; 600.057; 600.054; 600.076 | Approved | no | ||
| Call Number | XRV2014c; ADAS @ adas @ xrv2014c | Serial | 2455 | ||
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| Author | Antonio Hernandez; Stan Sclaroff; Sergio Escalera | ||||
| Title | Contextual rescoring for Human Pose Estimation | Type | Conference Article | ||
| Year | 2014 | Publication |
25th British Machine Vision Conference | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | A contextual rescoring method is proposed for improving the detection of body joints of a pictorial structure model for human pose estimation. A set of mid-level parts is incorporated in the model, and their detections are used to extract spatial and score-related features relative to other body joint hypotheses. A technique is proposed for the automatic discovery of a compact subset of poselets that covers a set of validation images
while maximizing precision. A rescoring mechanism is defined as a set-based boosting classifier that computes a new score for body joint detections, given its relationship to detections of other body joints and mid-level parts in the image. This new score complements the unary potential of a discriminatively trained pictorial structure model. Experiments on two benchmarks show performance improvements when considering the proposed mid-level image representation and rescoring approach in comparison with other pictorial structure-based approaches. |
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| Address | Nottingham; UK; September 2013 | ||||
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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 | BMVC | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | HSE2014 | Serial | 2525 | ||
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| Author | Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras | ||||
| Title | The Photometry of Intrinsic Images | Type | Conference Article | ||
| Year | 2014 | Publication |
27th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1494-1501 | ||
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| Abstract | Intrinsic characterization of scenes is often the best way to overcome the illumination variability artifacts that complicate most computer vision problems, from 3D reconstruction to object or material recognition. This paper examines the deficiency of existing intrinsic image models to accurately account for the effects of illuminant color and sensor characteristics in the estimation of intrinsic images and presents a generic framework which incorporates insights from color constancy research to the intrinsic image decomposition problem. The proposed mathematical formulation includes information about the color of the illuminant and the effects of the camera sensors, both of which modify the observed color of the reflectance of the objects in the scene during the acquisition process. By modeling these effects, we get a “truly intrinsic” reflectance image, which we call absolute reflectance, which is invariant to changes of illuminant or camera sensors. This model allows us to represent a wide range of intrinsic image decompositions depending on the specific assumptions on the geometric properties of the scene configuration and the spectral properties of the light source and the acquisition system, thus unifying previous models in a single general framework. We demonstrate that even partial information about sensors improves significantly the estimated reflectance images, thus making our method applicable for a wide range of sensors. We validate our general intrinsic image framework experimentally with both synthetic data and natural images. | ||||
| Address | Columbus; Ohio; USA; June 2014 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPR | ||
| Notes | CIC; 600.052; 600.051; 600.074 | Approved | no | ||
| Call Number | Admin @ si @ SPB2014 | Serial | 2506 | ||
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| Author | M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer | ||||
| Title | Adaptive color attributes for real-time visual tracking | Type | Conference Article | ||
| Year | 2014 | Publication |
27th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1090 - 1097 | ||
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| Abstract | Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object
recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power. This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames per second. |
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| Address | Nottingham; UK; September 2014 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPR | ||
| Notes | CIC; LAMP; 600.074; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ DKF2014 | Serial | 2509 | ||
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| Author | B. Zhou; Agata Lapedriza; J. Xiao; A. Torralba; A. Oliva | ||||
| Title | Learning Deep Features for Scene Recognition using Places Database | Type | Conference Article | ||
| Year | 2014 | Publication |
28th Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 487-495 | ||
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| Address | Montreal; Canada; December 2014 | ||||
| Corporate Author | Thesis | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | NIPS | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ ZLX2014 | Serial | 2621 | ||
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| Author | Jorge Bernal; Joan M. Nuñez; F. Javier Sanchez; Fernando Vilariño | ||||
| Title | Polyp Segmentation Method in Colonoscopy Videos by means of MSA-DOVA Energy Maps Calculation | Type | Conference Article | ||
| Year | 2014 | Publication |
3rd MICCAI Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging | Abbreviated Journal | |
| Volume | 8680 | Issue | Pages | 41-49 | |
| Keywords | Image segmentation; Polyps; Colonoscopy; Valley information; Energy maps | ||||
| Abstract | In this paper we present a novel polyp region segmentation method for colonoscopy videos. Our method uses valley information associated to polyp boundaries in order to provide an initial segmentation. This first segmentation is refined to eliminate boundary discontinuities caused by image artifacts or other elements of the scene. Experimental results over a publicly annotated database show that our method outperforms both general and specific segmentation methods by providing more accurate regions rich in polyp content. We also prove how image preprocessing is needed to improve final polyp region segmentation. | ||||
| Address | Boston; USA; September 2014 | ||||
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| Area | Expedition | Conference | CLIP | ||
| Notes | MV; 600.060; 600.044; 600.047;SIAI | Approved | no | ||
| Call Number | Admin @ si @ BNS2014 | Serial | 2502 | ||
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| Author | Alejandro Tabas; Emili Balaguer-Ballester; Laura Igual | ||||
| Title | Spatial Discriminant ICA for RS-fMRI characterisation | Type | Conference Article | ||
| Year | 2014 | Publication |
4th International Workshop on Pattern Recognition in Neuroimaging | Abbreviated Journal | |
| Volume | Issue | Pages | 1-4 | ||
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| Abstract | Resting-State fMRI (RS-fMRI) is a brain imaging technique useful for exploring functional connectivity. A major point of interest in RS-fMRI analysis is to isolate connectivity patterns characterising disorders such as for instance ADHD. Such characterisation is usually performed in two steps: first, all connectivity patterns in the data are extracted by means of Independent Component Analysis (ICA); second, standard statistical tests are performed over the extracted patterns to find differences between control and clinical groups. In this work we introduce a novel, single-step, approach for this problem termed Spatial Discriminant ICA. The algorithm can efficiently isolate networks of functional connectivity characterising a clinical group by combining ICA and a new variant of the Fisher’s Linear Discriminant also introduced in this work. As the characterisation is carried out in a single step, it potentially provides for a richer characterisation of inter-class differences. The algorithm is tested using synthetic and real fMRI data, showing promising results in both experiments. | ||||
| Address | Tübingen; June 2014 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-4799-4150-6 | Medium | ||
| Area | Expedition | Conference | PRNI | ||
| Notes | OR;MILAB | Approved | no | ||
| Call Number | Admin @ si @ TBI2014 | Serial | 2493 | ||
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| Author | Joan Arnedo-Moreno; D. Bañeres; Xavier Baro; S. Caballe; S. Guerrero; L. Porta; J. Prieto | ||||
| Title | Va-ID: A trust-based virtual assessment system | Type | Conference Article | ||
| Year | 2014 | Publication |
6th International Conference on Intelligent Networking and Collaborative Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 328 - 335 | ||
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| Abstract | Even though online education is a very important pillar of lifelong education, institutions are still reluctant to wager for a fully online educational model. At the end, they keep relying on on-site assessment systems, mainly because fully virtual alternatives do not have the deserved social recognition or credibility. Thus, the design of virtual assessment systems that are able to provide effective proof of student authenticity and authorship and the integrity of the activities in a scalable and cost efficient manner would be very helpful. This paper presents ValID, a virtual assessment approach based on a continuous trust level evaluation between students and the institution. The current trust level serves as the main mechanism to dynamically decide which kind of controls a given student should be subjected to, across different courses in a degree. The main goal is providing a fair trade-off between security, scalability and cost, while maintaining the perceived quality of the educational model. | ||||
| Address | Salerna; Italy; September 2014 | ||||
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| ISSN | ISBN | 978-1-4799-6386-7 | Medium | ||
| Area | Expedition | Conference | INCOS | ||
| Notes | OR; HuPBA;MV | Approved | no | ||
| Call Number | Admin @ si @ ABB2014 | Serial | 2620 | ||
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| Author | Adriana Romero; Carlo Gatta; Gustavo Camps-Valls | ||||
| Title | Unsupervised Deep Feature Extraction Of Hyperspectral Images | Type | Conference Article | ||
| Year | 2014 | Publication |
6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Convolutional networks; deep learning; sparse learning; feature extraction; hyperspectral image classification | ||||
| Abstract | This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images. Deep convolutional hierarchical representations are learned and then used for pixel classification. Features in lower layers present less abstract representations of data, while higher layers represent more abstract and complex characteristics. We successfully illustrate the performance of the extracted representations in a challenging AVIRIS hyperspectral image classification problem, compared to standard dimensionality reduction methods like principal component analysis (PCA) and its kernel counterpart (kPCA). The proposed method largely outperforms the previous state-ofthe-art results on the same experimental setting. Results show that single layer networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels. Regarding the deep architecture, we can conclude that: (1) additional layers in a deep architecture significantly improve the performance w.r.t. single layer variants; (2) the max-pooling step in each layer is mandatory to achieve satisfactory results; and (3) the performance gain w.r.t. the number of layers is upper bounded, since the spatial resolution is reduced at each pooling, resulting in too spatially coarse output features. | ||||
| Address | Lausanne; Switzerland; June 2014 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | WHISPERS | ||
| Notes | MILAB; LAMP; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ RGC2014 | Serial | 2513 | ||
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| Author | Oualid M. Benkarim; Petia Radeva; Laura Igual | ||||
| Title | Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation | Type | Conference Article | ||
| Year | 2014 | Publication |
8th Conference on Articulated Motion and Deformable Objects | Abbreviated Journal | |
| Volume | 8563 | Issue | Pages | 138-147 | |
| Keywords | MRI segmentation; sparse representation; discriminative dic- tionary learning; multiclass classi cation | ||||
| Abstract | The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images (MRI) still remains a challenging task. In this paper, we address this problem using sparse representation and discriminative dictionary learning, which have shown promising results in compression, image denoising and recently in MRI segmentation. Particularly, we use multiclass dictionaries learned from a set of brain atlases to simultaneously segment multiple subcortical structures.
We also impose dictionary atoms to be specialized in one given class using label consistent K-SVD, which can alleviate the bias produced by unbalanced libraries, present when dealing with small structures. The proposed method is compared with other state of the art approaches for the segmentation of the Basal Ganglia of 35 subjects of a public dataset. The promising results of the segmentation method show the eciency of the multiclass discriminative dictionary learning algorithms in MRI segmentation problems. |
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| Address | Palma de Mallorca; July 2014 | ||||
| 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 | 978-3-319-08848-8 | Medium | |
| Area | Expedition | Conference | AMDO | ||
| Notes | MILAB; OR | Approved | no | ||
| Call Number | Admin @ si @ BRI2014 | Serial | 2494 | ||
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| Author | Marc Bolaños; Maite Garolera; Petia Radeva | ||||
| Title | Video Segmentation of Life-Logging Videos | Type | Conference Article | ||
| Year | 2014 | Publication |
8th Conference on Articulated Motion and Deformable Objects | Abbreviated Journal | |
| Volume | 8563 | Issue | Pages | 1-9 | |
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| Area | Expedition | Conference | AMDO | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ BGR2014 | Serial | 2558 | ||
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| Author | Enric Marti; Antoni Gurgui; Debora Gil; Aura Hernandez-Sabate; Jaume Rocarias; Ferran Poveda | ||||
| Title | ABP on line: Seguimiento, estregas y evaluación en aprendizaje basado en proyectos | Type | Miscellaneous | ||
| Year | 2014 | Publication |
8th International Congress on University Teaching and Innovation | Abbreviated Journal | |
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| Address | Tarragona; juliol 2014 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CIDUI | ||
| Notes | IAM; ADAS; 600.076; 600.063; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ MGG2014 | Serial | 2457 | ||
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| Author | Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; Jaume Rocarias; Debora Gil | ||||
| Title | Evaluación automática de prácticas en Moodle para el aprendizaje autónomo en Ingenierías | Type | Miscellaneous | ||
| Year | 2014 | Publication |
8th International Congress on University Teaching and Innovation | Abbreviated Journal | |
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| Address | Tarragona; juliol 2014 | ||||
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| Area | Expedition | Conference | CIDUI | ||
| Notes | IAM; 600.075;DAG | Approved | no | ||
| Call Number | Admin @ si @ SRM2014 | Serial | 2458 | ||
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| Author | Patricia Marquez; Debora Gil; R.Mester; Aura Hernandez-Sabate | ||||
| Title | Local Analysis of Confidence Measures for Optical Flow Quality Evaluation | Type | Conference Article | ||
| Year | 2014 | Publication |
9th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
| Volume | 3 | Issue | Pages | 450-457 | |
| Keywords | Optical Flow; Confidence Measure; Performance Evaluation. | ||||
| Abstract | Optical Flow (OF) techniques facing the complexity of real sequences have been developed in the last years. Even using the most appropriate technique for our specific problem, at some points the output flow might fail to achieve the minimum error required for the system. Confidence measures computed from either input data or OF output should discard those points where OF is not accurate enough for its further use. It follows that evaluating the capabilities of a confidence measure for bounding OF error is as important as the definition
itself. In this paper we analyze different confidence measures and point out their advantages and limitations for their use in real world settings. We also explore the agreement with current tools for their evaluation of confidence measures performance. |
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| Address | Lisboa; January 2014 | ||||
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| Area | Expedition | Conference | VISAPP | ||
| Notes | IAM; ADAS; 600.044; 600.060; 600.057; 601.145; 600.076; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ MGM2014 | Serial | 2432 | ||
| Permanent link to this record | |||||