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Francesco Ciompi; Oriol Pujol; Petia Radeva |
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ECOC-DRF: Discriminative random fields based on error correcting output codes |
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2014 |
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Pattern Recognition |
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PR |
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47 |
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6 |
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2193-2204 |
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Discriminative random fields; Error-correcting output codes; Multi-class classification; Graphical models |
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We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments. |
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LAMP; HuPBA; MILAB; 605.203; 600.046; 601.043; 600.079 |
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Admin @ si @ CPR2014b |
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2470 |
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Author |
Marc Bolaños; Mariella Dimiccoli; Petia Radeva |
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Title |
Towards Storytelling from Visual Lifelogging: An Overview |
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2017 |
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IEEE Transactions on Human-Machine Systems |
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THMS |
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47 |
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1 |
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77 - 90 |
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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 |
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Admin @ si @ BDR2017 |
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2712 |
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Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
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A Genetic-based Subspace Analysis Method for Improving Error-Correcting Output Coding |
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2013 |
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Pattern Recognition |
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PR |
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46 |
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10 |
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2830-2839 |
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Error Correcting Output Codes; Evolutionary computation; Multiclass classification; Feature subspace; Ensemble classification |
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Two key factors affecting the performance of Error Correcting Output Codes (ECOC) in multiclass classification problems are the independence of binary classifiers and the problem-dependent coding design. In this paper, we propose an evolutionary algorithm-based approach to the design of an application-dependent codematrix in the ECOC framework. The central idea of this work is to design a three-dimensional codematrix, where the third dimension is the feature space of the problem domain. In order to do that, we consider the feature space in the design process of the codematrix with the aim of improving the independence and accuracy of binary classifiers. The proposed method takes advantage of some basic concepts of ensemble classification, such as diversity of classifiers, and also benefits from the evolutionary approach for optimizing the three-dimensional codematrix, taking into account the problem domain. We provide a set of experimental results using a set of benchmark datasets from the UCI Machine Learning Repository, as well as two real multiclass Computer Vision problems. Both sets of experiments are conducted using two different base learners: Neural Networks and Decision Trees. The results show that the proposed method increases the classification accuracy in comparison with the state-of-the-art ECOC coding techniques. |
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Elsevier |
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0031-3203 |
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HuPBA;MILAB |
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Admin @ si @ BGE2013a |
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2247 |
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Frederic Sampedro; Sergio Escalera; Anna Puig |
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Iterative Multiclass Multiscale Stacked Sequential Learning: definition and application to medical volume segmentation |
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2014 |
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Pattern Recognition Letters |
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PRL |
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46 |
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1-10 |
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Machine learning; Sequential learning; Multi-class problems; Contextual learning; Medical volume segmentation |
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In this work we present the iterative multi-class multi-scale stacked sequential learning framework (IMMSSL), a novel learning scheme that is particularly suited for medical volume segmentation applications. This model exploits the inherent voxel contextual information of the structures of interest in order to improve its segmentation performance results. Without any feature set or learning algorithm prior assumption, the proposed scheme directly seeks to learn the contextual properties of a region from the predicted classifications of previous classifiers within an iterative scheme. Performance results regarding segmentation accuracy in three two-class and multi-class medical volume datasets show a significant improvement with respect to state of the art alternatives. Due to its easiness of implementation and its independence of feature space and learning algorithm, the presented machine learning framework could be taken into consideration as a first choice in complex volume segmentation scenarios. |
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HuPBA;MILAB |
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Admin @ si @ SEP2014 |
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2550 |
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Miguel Angel Bautista; Antonio Hernandez; Sergio Escalera; Laura Igual; Oriol Pujol; Josep Moya; Veronica Violant; Maria Teresa Anguera |
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A Gesture Recognition System for Detecting Behavioral Patterns of ADHD |
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2016 |
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IEEE Transactions on System, Man and Cybernetics, Part B |
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TSMCB |
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46 |
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1 |
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136-147 |
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Gesture Recognition; ADHD; Gaussian Mixture Models; Convex Hulls; Dynamic Time Warping; Multi-modal RGB-Depth data |
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We present an application of gesture recognition using an extension of Dynamic Time Warping (DTW) to recognize behavioural patterns of Attention Deficit Hyperactivity Disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either GMMs or an approximation of Convex Hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intra-class gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioural patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multi-modal dataset (RGB plus Depth) of ADHD children recordings with behavioural patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context. |
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HuPBA; MILAB; |
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Admin @ si @ BHE2016 |
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2566 |
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