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Mateusz Pyla; Kamil Deja; Bartłomiej Twardowski; Tomasz Trzcinski |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Bayesian Flow Networks in Continual Learning |
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2023 |
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arxiv |
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Bayesian Flow Networks (BFNs) has been recently proposed as one of the most promising direction to universal generative modelling, having ability to learn any of the data type. Their power comes from the expressiveness of neural networks and Bayesian inference which make them suitable in the context of continual learning. We delve into the mechanics behind BFNs and conduct the experiments to empirically verify the generative capabilities on non-stationary data. |
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LAMP |
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Admin @ si @ PDT2023 |
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3972 |
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Author |
X. Orriols; Andrew Willis; X. Binefa; David B. Cooper |
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Bayesian estimation of axial symmetries from partial data, a generative model approach |
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2000 |
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CVC Technical Report #49 |
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DAG @ dag @ OWB2000 |
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536 |
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Author |
Eduardo Aguilar; Bhalaji Nagarajan; Beatriz Remeseiro; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Bayesian deep learning for semantic segmentation of food images |
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2022 |
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Computers and Electrical Engineering |
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CEE |
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103 |
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108380 |
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Deep learning; Uncertainty quantification; Bayesian inference; Image segmentation; Food analysis |
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Deep learning has provided promising results in various applications; however, algorithms tend to be overconfident in their predictions, even though they may be entirely wrong. Particularly for critical applications, the model should provide answers only when it is very sure of them. This article presents a Bayesian version of two different state-of-the-art semantic segmentation methods to perform multi-class segmentation of foods and estimate the uncertainty about the given predictions. The proposed methods were evaluated on three public pixel-annotated food datasets. As a result, we can conclude that Bayesian methods improve the performance achieved by the baseline architectures and, in addition, provide information to improve decision-making. Furthermore, based on the extracted uncertainty map, we proposed three measures to rank the images according to the degree of noisy annotations they contained. Note that the top 135 images ranked by one of these measures include more than half of the worst-labeled food images. |
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October 2022 |
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Science Direct |
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MILAB |
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Admin @ si @ ANR2022 |
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3763 |
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Jordi Vitria; M. Bressan; Petia Radeva |
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Bayesian classification of cork stoppers using class-conditional independent component analysis |
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2006 |
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IEEE Transactions on Systems, Man and Cybernetics (Part C), 36(6) |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ VBR2006 |
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723 |
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Jordi Vitria; M. Bressan; Petia Radeva |
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Bayesian classification of cork stoppers using class-conditional independent component analysis |
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2007 |
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IEEE Transactions on Systems, Man and Cybernetics (Part C), 37(1): 32–38 (ISI 0,482) |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ VBR2007 |
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795 |
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Petia Radeva; M. Bressan; A. Tovar; Jordi Vitria |
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Bayesian Classification for Inspection of Industrial Products. |
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2002 |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ RBT2002a |
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285 |
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Petia Radeva; M. Bressan; A. Tovar; Jordi Vitria |
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Bayesian Classification for Inspection of Industrial Products. |
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2002 |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ RBT2002c |
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316 |
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Author |
Alejandro Cartas; Juan Marin; Petia Radeva; Mariella Dimiccoli |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Batch-based activity recognition from egocentric photo-streams revisited |
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Journal Article |
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2018 |
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Pattern Analysis and Applications |
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PAA |
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21 |
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4 |
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953–965 |
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Egocentric vision; Lifelogging; Activity recognition; Deep learning; Recurrent neural networks |
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Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a late fusion ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high-level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85%, outperforming state-of-the-art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average. |
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MILAB; no proj |
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Admin @ si @ CMR2018 |
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3186 |
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Author |
Alejandro Cartas; Mariella Dimiccoli; Petia Radeva |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Batch-based activity recognition from egocentric photo-streams |
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2017 |
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1st International workshop on Egocentric Perception, Interaction and Computing |
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Activity recognition from long unstructured egocentric photo-streams has several applications in assistive technology such as health monitoring and frailty detection, just to name a few. However, one of its main technical challenges is to deal with the low frame rate of wearable photo-cameras, which causes abrupt appearance changes between consecutive frames. In consequence, important discriminatory low-level features from motion such as optical flow cannot be estimated. In this paper, we present a batch-driven approach for training a deep learning architecture that strongly rely on Long short-term units to tackle this problem. We propose two different implementations of the same approach that process a photo-stream sequence using batches of fixed size with the goal of capturing the temporal evolution of high-level features. The main difference between these implementations is that one explicitly models consecutive batches by overlapping them. Experimental results over a public dataset acquired by three users demonstrate the validity of the proposed architectures to exploit the temporal evolution of convolutional features over time without relying on event boundaries. |
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Venice; Italy; October 2017; |
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ICCV - EPIC |
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MILAB; no menciona |
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Admin @ si @ CDR2017 |
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3023 |
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Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Banknote counterfeit detection through background texture printing analysis |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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This paper is focused on the detection of counterfeit photocopy banknotes. The main difficulty is to work on a real industrial scenario without any constraint about the acquisition device and with a single image. The main contributions of this paper are twofold: first the adaptation and performance evaluation of existing approaches to classify the genuine and photocopy banknotes using background texture printing analysis, which have not been applied into this context before. Second, a new dataset of Euro banknotes images acquired with several cameras under different luminance conditions to evaluate these methods. Experiments on the proposed algorithms show that mixing SIFT features and sparse coding dictionaries achieves quasi perfect classification using a linear SVM with the created dataset. Approaches using dictionaries to cover all possible texture variations have demonstrated to be robust and outperform the state-of-the-art methods using the proposed benchmark. |
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Rumania; May 2016 |
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DAS |
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DAG; 600.061; 601.269; 600.097 |
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Admin @ si @ BRL2016 |
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2950 |
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Laura Lopez-Fuentes; Andrew Bagdanov; Joost Van de Weijer; Harald Skinnemoen |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Bandwidth Limited Object Recognition in High Resolution Imagery |
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2017 |
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IEEE Winter conference on Applications of Computer Vision |
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This paper proposes a novel method to optimize bandwidth usage for object detection in critical communication scenarios. We develop two operating models of active information seeking. The first model identifies promising regions in low resolution imagery and progressively requests higher resolution regions on which to perform recognition of higher semantic quality. The second model identifies promising regions in low resolution imagery while simultaneously predicting the approximate location of the object of higher semantic quality. From this general framework, we develop a car recognition system via identification of its license plate and evaluate the performance of both models on a car dataset that we introduce. Results are compared with traditional JPEG compression and demonstrate that our system saves up to one order of magnitude of bandwidth while sacrificing little in terms of recognition performance. |
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Santa Rosa; CA; USA; March 2017 |
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LAMP; 600.068; 600.109; 600.084; 600.106; 600.079; 600.120 |
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Admin @ si @ LBW2017 |
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2973 |
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Enric Marti; Debora Gil; Marc Vivet ; Carme Julia |
![download PDF file pdf](img/file_PDF.gif)
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Balance de cuatro años de experiencia en la implantación de la metodología de Aprendizaje Basado en Proyectos en la asignatura de Gráficos por Computador en ingeniería Informática |
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2008 |
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Actas V Jornadas Internacionales de Innovación Universitaria |
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Aprendizaje cooperativo; aprendizaje basado en proyectos; experiencias docentes. |
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En este trabajo se presentan los resultados de la aplicación de la metodología del aprendizaje cooperativo a la docencia de dos asignaturas de programación en ingeniería informática. ‘Algoritmos y programación’ y ‘Lenguajes de programación’ son dos asignaturas complementarias que se organizan entorno a un proyecto común que engloba los contenidos de ambas asignaturas. En la docencia de una parte muy importante de estas asignaturas, la metodología del aprendizaje cooperativo se ha adaptado a sus características específicas. Como muestra de esta adaptación presentamos dos ejemplos de las actividades desarrolladas dentro de la docencia de estas asignaturas. Después de tres años de aplicación, el análisis a nivel cualitativo y cuantitativo de los resultados muestra que éstos son muy satisfactorios y que la aplicación del método cooperativo ha mejorado de forma considerable el rendimiento de los alumnos en ambas asignaturas. |
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IAM;ADAS |
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IAM @ iam @ MGV2008a |
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1598 |
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Enric Marti; Debora Gil; Marc Vivet; Carme Julia |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Balance de cuatro años de experiencia en la implantación de la metodología de Aprendizaje Basado en Proyectos en la asignatura de Gráficos por Computador en ingeniería Informática |
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2008 |
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VIII Jornadas de Innovación Universitaria |
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IAM; ADAS |
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IAM @ iam @ MGV2008b |
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1599 |
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Santiago Segui; Laura Igual; Jordi Vitria |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Bagged One Class Classifiers in the Presence of Outliers |
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2013 |
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International Journal of Pattern Recognition and Artificial Intelligence |
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IJPRAI |
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27 |
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5 |
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1350014-1350035 |
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One-class Classifier; Ensemble Methods; Bagging and Outliers |
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The problem of training classifiers only with target data arises in many applications where non-target data are too costly, difficult to obtain, or not available at all. Several one-class classification methods have been presented to solve this problem, but most of the methods are highly sensitive to the presence of outliers in the target class. Ensemble methods have therefore been proposed as a powerful way to improve the classification performance of binary/multi-class learning algorithms by introducing diversity into classifiers.
However, their application to one-class classification has been rather limited. In
this paper, we present a new ensemble method based on a non-parametric weighted bagging strategy for one-class classification, to improve accuracy in the presence of outliers. While the standard bagging strategy assumes a uniform data distribution, the method we propose here estimates a probability density based on a forest structure of the data. This assumption allows the estimation of data distribution from the computation of simple univariate and bivariate kernel densities. Experiments using original and noisy versions of 20 different datasets show that bagging ensemble methods applied to different one-class classifiers outperform base one-class classification methods. Moreover, we show that, in noisy versions of the datasets, the non-parametric weighted bagging strategy we propose outperforms the classical bagging strategy in a statistically significant way. |
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OR; 600.046;MV |
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Admin @ si @ SIV2013 |
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2256 |
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Author |
Maedeh Aghaei; Petia Radeva |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Bag-of-Tracklets for Person Tracking in Life-Logging Data |
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Conference Article |
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2014 |
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17th International Conference of the Catalan Association for Artificial Intelligence |
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269 |
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35-44 |
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By increasing popularity of wearable cameras, life-logging data analysis is becoming more and more important and useful to derive significant events out of this substantial collection of images. In this study, we introduce a new tracking method applied to visual life-logging, called bag-of-tracklets, which is based on detecting, localizing and tracking of people. Given the low spatial and temporal resolution of the image data, our model generates and groups tracklets in a unsupervised framework and extracts image sequences of person appearance according to a similarity score of the bag-of-tracklets. The model output is a meaningful sequence of events expressing human appearance and tracking them in life-logging data. The achieved results prove the robustness of our model in terms of efficiency and accuracy despite the low spatial and temporal resolution of the data. |
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978-1-61499-451-0 |
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CCIA |
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MILAB |
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Admin @ si @ AgR2015 |
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2607 |
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