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| Author | Maria Salamo; Sergio Escalera | ||||
| Title | Increasing Retrieval Quality in Conversational Recommenders | Type | Journal Article | ||
| Year | 2011 | Publication | IEEE Transactions on Knowledge and Data Engineering | Abbreviated Journal | TKDE |
| Volume | 99 | Issue | Pages | 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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| Publisher | IEEE | Place of Publication | Editor | ||
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| ISSN | 1041-4347 | ISBN | Medium | ||
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Notes  |
MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ SaE2011 | Serial | 1713 | ||
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| Author | Oscar Amoros; Sergio Escalera; Anna Puig | ||||
| Title | Adaboost GPU-based Classifier for Direct Volume Rendering | Type | Conference Article | ||
| Year | 2011 | Publication | International Conference on Computer Graphics Theory and Applications | Abbreviated Journal | |
| Volume | Issue | Pages | 215-219 | ||
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| Abstract | 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. | ||||
| Address | Algarve, Portugal | ||||
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| Area | Expedition | Conference | GRAPP | ||
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MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ AEP2011 | Serial | 1774 | ||
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| Author | Sergio Escalera; Ana Puig; Oscar Amoros; Maria Salamo | ||||
| Title | Intelligent GPGPU Classification in Volume Visualization: a framework based on Error-Correcting Output Codes | Type | Journal Article | ||
| Year | 2011 | Publication | Computer Graphics Forum | Abbreviated Journal | CGF |
| 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 | Approved | no | ||
| Call Number | Admin @ si @ EPA2011 | Serial | 1881 | ||
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| Author | Antonio Hernandez; Carlos Primo; Sergio Escalera | ||||
| Title | Automatic user interaction correction via Multi-label Graph cuts | Type | Conference Article | ||
| Year | 2011 | Publication | In ICCV 2011 1st IEEE International Workshop on Human Interaction in Computer Vision HICV | Abbreviated Journal | |
| Volume | Issue | Pages | 1276-1281 | ||
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| Abstract | 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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| ISSN | ISBN | 978-1-4673-0062-9 | Medium | ||
| Area | Expedition | Conference | HICV | ||
| Notes |
MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ HPE2011 | Serial | 1892 | ||
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| Author | Sergio Escalera | ||||
| Title | Human Behavior Analysis From Depth Maps | Type | Conference Article | ||
| Year | 2012 | Publication | 7th Conference on Articulated Motion and Deformable Objects | Abbreviated Journal | |
| Volume | 7378 | Issue | Pages | 282-292 | |
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| Abstract | Pose Recovery (PR) and Human Behavior Analysis (HBA) have been a main focus of interest from the beginnings of Computer Vision and Machine Learning. PR and HBA were originally addressed by the analysis of still images and image sequences. More recent strategies consisted of Motion Capture technology (MOCAP), based on the synchronization of multiple cameras in controlled environments; and the analysis of depth maps from Time-of-Flight (ToF) technology, based on range image recording from distance sensor measurements. Recently, with the appearance of the multi-modal RGBD information provided by the low cost Kinect \textsfTM sensor (from RGB and Depth, respectively), classical methods for PR and HBA have been redefined, and new strategies have been proposed. In this paper, the recent contributions and future trends of multi-modal RGBD data analysis for PR and HBA are reviewed and discussed. | ||||
| Address | Mallorca | ||||
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| Publisher | Springer Heidelberg | Place of Publication | Editor | F.J. Perales; R.B. Fisher; T.B. Moeslund | |
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| ISSN | 0302-9743 | ISBN | 978-3-642-31566-4 | Medium | |
| Area | Expedition | Conference | AMDO | ||
| Notes |
MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ Esc2012 | Serial | 2040 | ||
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| Author | Sergio Escalera | ||||
| Title | Coding and Decoding Design of ECOCs for Multi-class Pattern and Object Recognition A | Type | Book Whole | ||
| Year | 2008 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Many real problems require multi-class decisions. In the Pattern Recognition field,
many techniques have been proposed to deal with the binary problem. However, the extension of many 2-class classifiers to the multi-class case is a hard task. In this sense, Error-Correcting Output Codes (ECOC) demonstrated to be a powerful tool to combine any number of binary classifiers to model multi-class problems. But there are still many open issues about the capabilities of the ECOC framework. In this thesis, the two main stages of an ECOC design are analyzed: the coding and the decoding steps. We present different problem-dependent designs. These designs take advantage of the knowledge of the problem domain to minimize the number of classifiers, obtaining a high classification performance. On the other hand, we analyze the ECOC codification in order to define new decoding rules that take full benefit from the information provided at the coding step. Moreover, as a successful classification requires a rich feature set, new feature detection/extraction techniques are presented and evaluated on the new ECOC designs. The evaluation of the new methodology is performed on different real and synthetic data sets: UCI Machine Learning Repository, handwriting symbols, traffic signs from a Mobile Mapping System, Intravascular Ultrasound images, Caltech Repository data set or Chaga’s disease data set. The results of this thesis show that significant performance improvements are obtained on both traditional coding and decoding ECOC designs when the new coding and decoding rules are taken into account. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Petia Radeva;Oriol Pujol | |
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MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ Esc2008b | Serial | 2217 | ||
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| Author | Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva | ||||
| Title | Multi-class Binary Object Categorization using Blurred Shape Models | Type | Conference Article | ||
| Year | 2007 | Publication | Progress in Pattern Recognition, Image Analysis and Applications, 12th Iberoamerican Congress on Pattern | Abbreviated Journal | |
| Volume | 4756 | Issue | Pages | 773–782 | |
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| Series Editor | Series Title | Abbreviated Series Title | LCNS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-540-76724-4 | Medium | ||
| Area | Expedition | Conference | CIARP | ||
| Notes |
MILAB; DAG;HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EFP2007 | Serial | 911 | ||
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| Author | Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva | ||||
| Title | Circular Blurred Shape Model for Multiclass Symbol Recognition | Type | Journal Article | ||
| Year | 2011 | Publication | IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) | Abbreviated Journal | TSMCB |
| Volume | 41 | Issue | 2 | Pages | 497-506 |
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| Abstract | 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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| Series Volume | Series Issue | Edition | |||
| ISSN | 1083-4419 | ISBN | Medium | ||
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| Notes |
MILAB; DAG;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ EFP2011 | Serial | 1784 | ||
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| Author | Xavier Otazu; Oriol Pujol | ||||
| Title | Wavelet based approach to cluster analysis. Application on low dimensional data sets | Type | Journal Article | ||
| Year | 2006 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 27 | Issue | 14 | Pages | 1590–1605 |
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MILAB; CIC; HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ OtP2006 | Serial | 658 | ||
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| Author | Simon Jégou; Michal Drozdzal; David Vazquez; Adriana Romero; Yoshua Bengio | ||||
| Title | The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Semantic Segmentation | ||||
| Abstract | State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions.
Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train. In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets. |
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| Address | Honolulu; USA; July 2017 | ||||
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| Area | Expedition | Conference | CVPRW | ||
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MILAB; ADAS; 600.076; 600.085; 601.281 | Approved | no | ||
| Call Number | ADAS @ adas @ JDV2016 | Serial | 2866 | ||
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| Author | Pierluigi Casale; Oriol Pujol; Petia Radeva | ||||
| Title | Approximate polytope ensemble for one-class classification | Type | Journal Article | ||
| Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue | 2 | Pages | 854-864 |
| Keywords | One-class classification; Convex hull; High-dimensionality; Random projections; Ensemble learning | ||||
| Abstract | In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, the decision whether a point belongs to the convex hull model in high dimensional spaces is approximated by means of random projections and an ensemble decision process. Finally, a tiling strategy is proposed in order to model non-convex structures. Experimental results show that the proposed strategy is significantly better than state of the art one-class classification methods on over 200 datasets. | ||||
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MILAB; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ CPR2014a | Serial | 2469 | ||
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| Author | Marc Bolaños; Mariella Dimiccoli; Petia Radeva | ||||
| Title | Towards Storytelling from Visual Lifelogging: An Overview | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on Human-Machine Systems | Abbreviated Journal | THMS |
| Volume | 47 | Issue | 1 | Pages | 77 - 90 |
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| Abstract | Visual lifelogging consists of acquiring images that capture the daily experiences of the user by wearing a camera over a long period of time. The pictures taken offer considerable potential for knowledge mining concerning how people live their lives, hence, they open up new opportunities for many potential applications in fields including healthcare, security, leisure and
the quantified self. However, automatically building a story from a huge collection of unstructured egocentric data presents major challenges. This paper provides a thorough review of advances made so far in egocentric data analysis, and in view of the current state of the art, indicates new lines of research to move us towards storytelling from visual lifelogging. |
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MILAB; 601.235 | Approved | no | ||
| Call Number | Admin @ si @ BDR2017 | Serial | 2712 | ||
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| Author | Mariella Dimiccoli; Marc Bolaños; Estefania Talavera; Maedeh Aghaei; Stavri G. Nikolov; Petia Radeva | ||||
| Title | SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation | Type | Journal Article | ||
| Year | 2017 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 155 | Issue | Pages | 55-69 | |
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| Abstract | While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming processes. This paper addresses the problem of organizing egocentric photo streams acquired by a wearable camera into semantically meaningful segments. First, contextual and semantic information is extracted for each image by employing a Convolutional Neural Networks approach. Later, by integrating language processing, a vocabulary of concepts is defined in a semantic space. Finally, by exploiting the temporal coherence in photo streams, images which share contextual and semantic attributes are grouped together. The resulting temporal segmentation is particularly suited for further analysis, ranging from activity and event recognition to semantic indexing and summarization. Experiments over egocentric sets of nearly 17,000 images, show that the proposed approach outperforms state-of-the-art methods. | ||||
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MILAB; 601.235 | Approved | no | ||
| Call Number | Admin @ si @ DBT2017 | Serial | 2714 | ||
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| Author | Maria Oliver; G. Haro; Mariella Dimiccoli; B. Mazin; C. Ballester | ||||
| Title | A Computational Model for Amodal Completion | Type | Journal Article | ||
| Year | 2016 | Publication | Journal of Mathematical Imaging and Vision | Abbreviated Journal | JMIV |
| Volume | 56 | Issue | 3 | Pages | 511–534 |
| Keywords | Perception; visual completion; disocclusion; Bayesian model;relatability; Euler elastica | ||||
| Abstract | This paper presents a computational model to recover the most likely interpretation
of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth. Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling. |
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MILAB; 601.235 | Approved | no | ||
| Call Number | Admin @ si @ OHD2016b | Serial | 2745 | ||
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| Author | Maria Oliver; Gloria Haro; Mariella Dimiccoli; Baptiste Mazin; Coloma Ballester | ||||
| Title | A computational model of amodal completion | Type | Conference Article | ||
| Year | 2016 | Publication | SIAM Conference on Imaging Science | Abbreviated Journal | |
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| Abstract | This paper presents a computational model to recover the most likely interpretation of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth. Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling. | ||||
| Address | Albuquerque; New Mexico; USA; May 2016 | ||||
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MILAB; 601.235 | Approved | no | ||
| Call Number | Admin @ si @OHD2016a | Serial | 2788 | ||
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