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Author |
Maria Salamo; Sergio Escalera |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Increasing Retrieval Quality in Conversational Recommenders |
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Journal Article |
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Year |
2011 |
Publication |
IEEE Transactions on Knowledge and Data Engineering |
Abbreviated Journal |
TKDE |
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Volume |
99 |
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1-1 |
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Abstract |
IF JCR CCIA 2.286 2009 24/103
JCR Impact Factor 2010: 1.851
A major task of research in conversational recommender systems is personalization. Critiquing is a common and powerful form of feedback, where a user can express her feature preferences by applying a series of directional critiques over the recommendations instead of providing specific preference values. Incremental Critiquing is a conversational recommender system that uses critiquing as a feedback to efficiently personalize products. The expectation is that in each cycle the system retrieves the products that best satisfy the user’s soft product preferences from a minimal information input. In this paper, we present a novel technique that increases retrieval quality based on a combination of compatibility and similarity scores. Under the hypothesis that a user learns Turing the recommendation process, we propose two novel exponential reinforcement learning approaches for compatibility that take into account both the instant at which the user makes a critique and the number of satisfied critiques. Moreover, we consider that the impact of features on the similarity differs according to the preferences manifested by the user. We propose a global weighting approach that uses a common weight for nearest cases in order to focus on groups of relevant products. We show that our methodology significantly improves recommendation efficiency in four data sets of different sizes in terms of session length in comparison with state-of-the-art approaches. Moreover, our recommender shows higher robustness against noisy user data when compared to classical approaches |
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IEEE |
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1041-4347 |
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MILAB; HuPBA |
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no |
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Admin @ si @ SaE2011 |
Serial |
1713 |
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Author |
Oscar Amoros; Sergio Escalera; Anna Puig |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Adaboost GPU-based Classifier for Direct Volume Rendering |
Type |
Conference Article |
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Year |
2011 |
Publication |
International Conference on Computer Graphics Theory and Applications |
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215-219 |
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In volume visualization, the voxel visibitity and materials are carried out through an interactive editing of Transfer Function. In this paper, we present a two-level GPU-based labeling method that computes in times of rendering a set of labeled structures using the Adaboost machine learning classifier. In a pre-processing step, Adaboost trains a binary classifier from a pre-labeled dataset and, in each sample, takes into account a set of features. This binary classifier is a weighted combination of weak classifiers, which can be expressed as simple decision functions estimated on a single feature values. Then, at the testing stage, each weak classifier is independently applied on the features of a set of unlabeled samples. We propose an alternative representation of these classifiers that allow a GPU-based parallelizated testing stage embedded into the visualization pipeline. The empirical results confirm the OpenCL-based classification of biomedical datasets as a tough problem where an opportunity for further research emerges. |
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Algarve, Portugal |
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GRAPP |
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MILAB; HuPBA |
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no |
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Admin @ si @ AEP2011 |
Serial |
1774 |
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Author |
Sergio Escalera; Ana Puig; Oscar Amoros; Maria Salamo |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Intelligent GPGPU Classification in Volume Visualization: a framework based on Error-Correcting Output Codes |
Type |
Journal Article |
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Year |
2011 |
Publication |
Computer Graphics Forum |
Abbreviated Journal |
CGF |
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Volume |
30 |
Issue |
7 |
Pages |
2107-2115 |
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Abstract |
IF JCR 1.455 2010 25/99
In volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on-demand multiple regions of interest. These methods can be split into a two-level GPU-based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error-Correcting Output Codes (ECOC) framework. In a pre-processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre-labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi-class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU-OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy. |
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MILAB; HuPBA |
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no |
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Call Number |
Admin @ si @ EPA2011 |
Serial |
1881 |
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Author |
Antonio Hernandez; Carlos Primo; Sergio Escalera |
![goto web page (via DOI) doi](img/doi.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Automatic user interaction correction via Multi-label Graph cuts |
Type |
Conference Article |
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Year |
2011 |
Publication |
In ICCV 2011 1st IEEE International Workshop on Human Interaction in Computer Vision HICV |
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1276-1281 |
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Most applications in image segmentation requires from user interaction in order to achieve accurate results. However, user wants to achieve the desired segmentation accuracy reducing effort of manual labelling. In this work, we extend standard multi-label α-expansion Graph Cut algorithm so that it analyzes the interaction of the user in order to modify the object model and improve final segmentation of objects. The approach is inspired in the fact that fast user interactions may introduce some pixel errors confusing object and background. Our results with different degrees of user interaction and input errors show high performance of the proposed approach on a multi-label human limb segmentation problem compared with classical α-expansion algorithm. |
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978-1-4673-0062-9 |
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HICV |
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Notes ![sorted by Notes field, descending order (down)](img/sort_desc.gif) |
MILAB; HuPBA |
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no |
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Call Number |
Admin @ si @ HPE2011 |
Serial |
1892 |
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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Circular Blurred Shape Model for Multiclass Symbol Recognition |
Type |
Journal Article |
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Year |
2011 |
Publication |
IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) |
Abbreviated Journal |
TSMCB |
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Volume |
41 |
Issue |
2 |
Pages |
497-506 |
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In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations. |
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1083-4419 |
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MILAB; DAG;HuPBA |
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no |
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Call Number |
Admin @ si @ EFP2011 |
Serial |
1784 |
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Author |
Carlo Gatta; Simone Balocco; Victoria Martin Yuste; Ruben Leta; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Non-rigid Multi-modal Registration of Coronary Arteries Using SIFTflow |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
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Volume |
6669 |
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Pages |
159-166 |
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The fusion of clinically relevant information coming from different image modalities is an important topic in medical imaging. In particular, different cardiac imaging modalities provides complementary information for the physician: Computer Tomography Angiography (CTA) provides reliable pre-operative information on arteries geometry, even in the presence of chronic total occlusions, while X-Ray Angiography (XRA) allows intra-operative high resolution projections of a specific artery. The non-rigid registration of arteries between these two modalities is a difficult task. In this paper we propose the use of SIFTflow, in registering CTA and XRA images. At the best of our knowledge, this paper proposed SIFTflow as a XRay-CTA registration method for the first time in the literature. To highlight the arteries, so to guide the registration process, the well known Vesselness method has been employed. Results confirm that, to the aim of registration, the arteries must be highlighted and background objects removed as much as possible. Moreover, the comparison with the well known Free Form Deformation technique, suggests that SIFTflow has a great potential in the registration of multi-modal medical images. |
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Las Palmas de Gran Canaria. Spain |
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Springer Berlin Heidelberg |
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Berlin |
Editor |
Jordi Vitria; Joao Miguel Sanches; Mario Hernandez |
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LNCS |
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0302-9743 |
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978-3-642-21256-7 |
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IbPRIA |
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MILAB |
Approved |
no |
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Call Number |
Admin @ si @ GBM2011 |
Serial |
1752 |
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Author |
Simone Balocco; Carlo Gatta; Xavier Carrillo; J. Mauri; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Plaque Type, Plaque Burden and Wall Shear Stress Relation in Coronary Arteries Assessed by X-ray Angiography and Intravascular Ultrasound: a Qualitative Study |
Type |
Conference Article |
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Year |
2011 |
Publication |
14th International Symposium on Applied Sciences in Biomedical and Communication Technologies |
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In this paper, we present a complete framework that automatically provides fluid-dynamic and plaque analysis from IVUS and Angiographic sequences. Such framework is used to analyze, in three coronary arteries, the relation between wall shear stress with type and amount of plaque. Preliminary qualitative results show an inverse relation between the wall shear stress and the plaque burden, which is confirmed by the fact that the plaque growth is higher on the wall having concave curvature. Regarding the plaque type it was observed that regions having low shear stress are predominantly fibro-lipidic while the heavy calcifications are in general located in areas of the vessel having high WSS. |
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978-1-4503-0913-4 |
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ISABEL |
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MILAB |
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no |
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Call Number |
Admin @ si @ BGC2011b |
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1799 |
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Author |
E. Serradell; Adriana Romero; R. Leta; Carlo Gatta; Francesc Moreno-Noguer |
![goto web page url](img/www.gif)
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Title |
Simultaneous Correspondence and Non-Rigid 3D Reconstruction of the Coronary Tree from Single X-Ray Images |
Type |
Conference Article |
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Year |
2011 |
Publication |
13th IEEE International Conference on Computer Vision |
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850-857 |
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Barcelona |
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ICCV |
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Notes ![sorted by Notes field, descending order (down)](img/sort_desc.gif) |
MILAB |
Approved |
no |
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Call Number |
Admin @ si @ SRL2011 |
Serial |
1803 |
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Author |
Santiago Segui |
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Title |
Contributions to the Diagnosis of Intestinal Motility by Automatic Image Analysis |
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Book Whole |
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Year |
2011 |
Publication |
PhD Thesis, Universitat de Barcelona-CVC |
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In the early twenty first century Given Imaging Ltd. presented wireless capsule endoscopy (WCE) as a new technological breakthrough that allowed the visualization of
the intestine by using a small, swallowed camera. This small size device was received
with a high enthusiasm within the medical community, and until now, it is still one
of the medical devices with the highest use growth rate. WCE can be used as a novel
diagnostic tool that presents several clinical advantages, since it is non-invasive and
at the same time it provides, for the first time, a full picture of the small bowel morphology, contents and dynamics. Since its appearance, the WCE has been used to
detect several intestinal dysfunctions such as: polyps, ulcers and bleeding. However,
the visual analysis of WCE videos presents an important drawback: the long time
required by the physicians for proper video visualization. In this sense and regarding
to this limitation, the development of computer aided systems is required for the extensive use of WCE in the medical community.
The work presented in this thesis is a set of contributions for the automatic image
analysis and computer-aided diagnosis of intestinal motility disorders using WCE.
Until now, the diagnosis of small bowel motility dysfunctions was basically performed
by invasive techniques such as the manometry test, which can only be conducted at
some referral centers around the world owing to the complexity of the procedure and
the medial expertise required in the interpretation of the results.
Our contributions are divided in three main blocks:
1. Image analysis by computer vision techniques to detect events in the endoluminal WCE scene. Several methods have been proposed to detect visual events
such as: intestinal contractions, intestinal content, tunnel and wrinkles;
2. Machine learning techniques for the analysis and the manipulation of the data
from WCE. These methods have been proposed in order to overcome the problems that the analysis of WCE presents such as: video acquisition cost, unlabeled data and large number of data;
3. Two different systems for the computer-aided diagnosis of intestinal motility
disorders using WCE. The first system presents a fully automatic method that
aids at discriminating healthy subjects from patients with severe intestinal motor disorders like pseudo-obstruction or food intolerance. The second system presents another automatic method that models healthy subjects and discriminate them from mild intestinal motility patients. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Jordi Vitria |
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MILAB |
Approved |
no |
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Call Number |
Admin @ si @ Seg2011 |
Serial |
1836 |
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Permanent link to this record |
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Author |
Pierluigi Casale |
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Title |
Approximate Ensemble Methods for Physical Activity Recognition Applications |
Type |
Book Whole |
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2011 |
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PhD Thesis, Universitat de Barcelona-CVC |
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The main interest of this thesis focuses on computational methodologies able to
reduce the degree of complexity of learning algorithms and its application to physical
activity recognition.
Random Projections will be used to reduce the computational complexity in Multiple Classifier Systems. A new boosting algorithm and a new one-class classification
methodology have been developed. In both cases, random projections are used for
reducing the dimensionality of the problem and for generating diversity, exploiting in
this way the benefits that ensembles of classifiers provide in terms of performances
and stability. Moreover, the new one-class classification methodology, based on an ensemble strategy able to approximate a multidimensional convex-hull, has been proved
to over-perform state-of-the-art one-class classification methodologies.
The practical focus of the thesis is towards Physical Activity Recognition. A new
hardware platform for wearable computing application has been developed and used
for collecting data of activities of daily living allowing to study the optimal features
set able to successful classify activities.
Based on the classification methodologies developed and the study conducted on
physical activity classification, a machine learning architecture capable to provide a
continuous authentication mechanism for mobile-devices users has been worked out,
as last part of the thesis. The system, based on a personalized classifier, states on
the analysis of the characteristic gait patterns typical of each individual ensuring an
unobtrusive and continuous authentication mechanism |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Editor |
Oriol Pujol;Petia Radeva |
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MILAB |
Approved |
no |
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Call Number |
Admin @ si @ Cas2011 |
Serial |
1837 |
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Permanent link to this record |
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Author |
Arjan Gijsenij; Theo Gevers; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Computational Color Constancy: Survey and Experiments |
Type |
Journal Article |
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Year |
2011 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
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Volume |
20 |
Issue |
9 |
Pages |
2475-2489 |
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Keywords |
computational color constancy;computer vision application;gamut-based method;learning-based method;static method;colour vision;computer vision;image colour analysis;learning (artificial intelligence);lighting |
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Computational color constancy is a fundamental prerequisite for many computer vision applications. This paper presents a survey of many recent developments and state-of-the- art methods. Several criteria are proposed that are used to assess the approaches. A taxonomy of existing algorithms is proposed and methods are separated in three groups: static methods, gamut-based methods and learning-based methods. Further, the experimental setup is discussed including an overview of publicly available data sets. Finally, various freely available methods, of which some are considered to be state-of-the-art, are evaluated on two data sets. |
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1057-7149 |
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ISE;CIC |
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no |
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Call Number |
Admin @ si @ GGW2011 |
Serial |
1717 |
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Author |
Marco Pedersoli; Jordi Gonzalez; Andrew Bagdanov; Xavier Roca |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Efficient Discriminative Multiresolution Cascade for Real-Time Human Detection Applications |
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Journal Article |
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2011 |
Publication |
Pattern Recognition Letters |
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PRL |
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32 |
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13 |
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1581-1587 |
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Human detection is fundamental in many machine vision applications, like video surveillance, driving assistance, action recognition and scene understanding. However in most of these applications real-time performance is necessary and this is not achieved yet by current detection methods.
This paper presents a new method for human detection based on a multiresolution cascade of Histograms of Oriented Gradients (HOG) that can highly reduce the computational cost of detection search without affecting accuracy. The method consists of a cascade of sliding window detectors. Each detector is a linear Support Vector Machine (SVM) composed of HOG features at different resolutions, from coarse at the first level to fine at the last one.
In contrast to previous methods, our approach uses a non-uniform stride of the sliding window that is defined by the feature resolution and allows the detection to be incrementally refined as going from coarse-to-fine resolution. In this way, the speed-up of the cascade is not only due to the fewer number of features computed at the first levels of the cascade, but also to the reduced number of windows that need to be evaluated at the coarse resolution. Experimental results show that our method reaches a detection rate comparable with the state-of-the-art of detectors based on HOG features, while at the same time the detection search is up to 23 times faster. |
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Admin @ si @ PGB2011a |
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1707 |
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Author |
Ariel Amato; Mikhail Mozerov; Andrew Bagdanov; Jordi Gonzalez |
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Title |
Accurate Moving Cast Shadow Suppression Based on Local Color Constancy detection |
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Journal Article |
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Year |
2011 |
Publication |
IEEE Transactions on Image Processing |
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TIP |
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20 |
Issue |
10 |
Pages |
2954 - 2966 |
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This paper describes a novel framework for detection and suppression of properly shadowed regions for most possible scenarios occurring in real video sequences. Our approach requires no prior knowledge about the scene, nor is it restricted to specific scene structures. Furthermore, the technique can detect both achromatic and chromatic shadows even in the presence of camouflage that occurs when foreground regions are very similar in color to shadowed regions. The method exploits local color constancy properties due to reflectance suppression over shadowed regions. To detect shadowed regions in a scene, the values of the background image are divided by values of the current frame in the RGB color space. We show how this luminance ratio can be used to identify segments with low gradient constancy, which in turn distinguish shadows from foreground. Experimental results on a collection of publicly available datasets illustrate the superior performance of our method compared with the most sophisticated, state-of-the-art shadow detection algorithms. These results show that our approach is robust and accurate over a broad range of shadow types and challenging video conditions. |
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1057-7149 |
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Admin @ si @ AMB2011 |
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1716 |
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Author |
Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez |
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Title |
Determining the Best Suited Semantic Events for Cognitive Surveillance |
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Journal Article |
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Year |
2011 |
Publication |
Expert Systems with Applications |
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EXSY |
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38 |
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4 |
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4068–4079 |
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Cognitive surveillance; Event modeling; Content-based video retrieval; Ontologies; Advanced user interfaces |
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State-of-the-art systems on cognitive surveillance identify and describe complex events in selected domains, thus providing end-users with tools to easily access the contents of massive video footage. Nevertheless, as the complexity of events increases in semantics and the types of indoor/outdoor scenarios diversify, it becomes difficult to assess which events describe better the scene, and how to model them at a pixel level to fulfill natural language requests. We present an ontology-based methodology that guides the identification, step-by-step modeling, and generalization of the most relevant events to a specific domain. Our approach considers three steps: (1) end-users provide textual evidence from surveilled video sequences; (2) transcriptions are analyzed top-down to build the knowledge bases for event description; and (3) the obtained models are used to generalize event detection to different image sequences from the surveillance domain. This framework produces user-oriented knowledge that improves on existing advanced interfaces for video indexing and retrieval, by determining the best suited events for video understanding according to end-users. We have conducted experiments with outdoor and indoor scenes showing thefts, chases, and vandalism, demonstrating the feasibility and generalization of this proposal. |
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Elsevier |
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no |
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Admin @ si @ FBR2011a |
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1722 |
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Author |
Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez |
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Title |
Augmenting Video Surveillance Footage with Virtual Agents for Incremental Event Evaluation |
Type |
Journal Article |
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Year |
2011 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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32 |
Issue |
6 |
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878–889 |
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The fields of segmentation, tracking and behavior analysis demand for challenging video resources to test, in a scalable manner, complex scenarios like crowded environments or scenes with high semantics. Nevertheless, existing public databases cannot scale the presence of appearing agents, which would be useful to study long-term occlusions and crowds. Moreover, creating these resources is expensive and often too particularized to specific needs. We propose an augmented reality framework to increase the complexity of image sequences in terms of occlusions and crowds, in a scalable and controllable manner. Existing datasets can be increased with augmented sequences containing virtual agents. Such sequences are automatically annotated, thus facilitating evaluation in terms of segmentation, tracking, and behavior recognition. In order to easily specify the desired contents, we propose a natural language interface to convert input sentences into virtual agent behaviors. Experimental tests and validation in indoor, street, and soccer environments are provided to show the feasibility of the proposed approach in terms of robustness, scalability, and semantics. |
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Elsevier |
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Approved |
no |
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Call Number |
Admin @ si @ FBR2011b |
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1723 |
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