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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Miguel Angel Bautista; Antonio Hernandez; Victor Ponce; Xavier Perez Sala; Xavier Baro; Oriol Pujol; Cecilio Angulo; Sergio Escalera |
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Title |
Probability-based Dynamic TimeWarping for Gesture Recognition on RGB-D data |
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Conference Article |
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Year |
2012 |
Publication |
21st International Conference on Pattern Recognition International Workshop on Depth Image Analysis |
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7854 |
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126-135 |
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Dynamic Time Warping (DTW) is commonly used in gesture recognition tasks in order to tackle the temporal length variability of gestures. In the DTW framework, a set of gesture patterns are compared one by one to a maybe infinite test sequence, and a query gesture category is recognized if a warping cost below a certain threshold is found within the test sequence. Nevertheless, either taking one single sample per gesture category or a set of isolated samples may not encode the variability of such gesture category. In this paper, a probability-based DTW for gesture recognition is proposed. Different samples of the same gesture pattern obtained from RGB-Depth data are used to build a Gaussian-based probabilistic model of the gesture. Finally, the cost of DTW has been adapted accordingly to the new model. The proposed approach is tested in a challenging scenario, showing better performance of the probability-based DTW in comparison to state-of-the-art approaches for gesture recognition on RGB-D data. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-40302-6 |
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WDIA |
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MILAB; OR;HuPBA;MV |
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no |
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Admin @ si @ BHP2012 |
Serial |
2120 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Miguel Angel Bautista; Oriol Pujol; Fernando De la Torre; Sergio Escalera |
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Title |
Error-Correcting Factorization |
Type |
Journal Article |
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Year |
2018 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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40 |
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Pages |
2388-2401 |
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Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi- class problem is decoupled into a set of binary problems that are solved independently. However, literature defines a general error-correcting capability for ECOCs without analyzing how it distributes among classes, hindering a deeper analysis of pair-wise error-correction. To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method, our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes. (II) We derive the optimal code length of an ECOC using rank properties of the design matrix. (III) ECF is formulated as a discrete optimization problem, and a relaxed solution is found using an efficient constrained block coordinate descent approach. (IV) Enabled by the flexibility introduced with the design matrix we propose to allocate the error-correction on classes that are prone to confusion. Experimental results in several databases show that when allocating the error-correction to confusable classes ECF outperforms state-of-the-art approaches. |
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0162-8828 |
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HuPBA; no menciona |
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no |
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Admin @ si @ BPT2018 |
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3015 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Miguel Angel Bautista; Oriol Pujol; Xavier Baro; Sergio Escalera |
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Title |
Introducing the Separability Matrix for Error Correcting Output Codes Coding |
Type |
Conference Article |
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Year |
2011 |
Publication |
10th International conference on Multiple Classifier Systems |
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6713 |
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227-236 |
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Error Correcting Output Codes (ECOC) have demonstrate to be a powerful tool for treating multi-class problems. Nevertheless, predefined ECOC designs may not benefit from Error-correcting principles for particular multi-class data. In this paper, we introduce the Separability matrix as a tool to study and enhance designs for ECOC coding. In addition, a novel problem-dependent coding design based on the Separability matrix is tested over a wide set of challenging multi-class problems, obtaining very satisfactory results. |
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Napoles, Italy |
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Springer-Verlag Berlin Heidelberg |
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Carlo Sansone; Josef Kittler; Fabio Roli |
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978-3-642-21556-8 |
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MCS |
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MILAB; OR;HuPBA;MV |
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no |
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Call Number |
Admin @ si @ BPB2011a |
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1771 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Miguel Angel Bautista; Oriol Pujol; Xavier Baro; Sergio Escalera |
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Title |
Introducing the Separability Matrix for Error Correcting Output Codes Coding |
Type |
Conference Article |
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Year |
2011 |
Publication |
10th International Conference on Multiple Classifier Systems |
Abbreviated Journal |
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Volume |
6713 |
Issue |
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Pages |
227-236 |
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Abstract |
Error Correcting Output Codes (ECOC) have demonstrate to be a powerful tool for treating multi-class problems. Nevertheless, predefined ECOC designs may not benefit from Error-correcting principles for particular multi-class data. In this paper, we introduce the Separability matrix as a tool to study and enhance designs for ECOC coding. In addition, a novel problem-dependent coding design based on the Separability matrix is tested over a wide set of challenging multi-class problems, obtaining very satisfactory results. |
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Napoles, Italy |
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Springer-Verlag Berlin, Heidelberg |
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Carlo Sansone; Josef Kittler; Fabio Roli |
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0302-9743 |
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978-3-642-21556-8 |
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MCS |
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MILAB; OR;HuPBA;MV |
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no |
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Call Number |
Admin @ si @ BPB2011b |
Serial |
1887 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Miguel Angel Bautista; Sergio Escalera; Oriol Pujol |
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Title |
On the Design of an ECOC-Compliant Genetic Algorithm |
Type |
Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
47 |
Issue |
2 |
Pages |
865-884 |
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Abstract |
Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches. |
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HuPBA;MILAB |
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no |
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Admin @ si @ BEP2013 |
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2254 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Oriol Pujol; Jordi Vitria; Petia Radeva |
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Title |
Compact Evolutive Design of Error-Correcting Output Codes. Supervised and Unsupervised Ensemble Methods and Applications |
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Conference Article |
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2010 |
Publication |
European Conference on Machine Learning |
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I |
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119-128 |
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ECML |
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MILAB; OR;HUPBA;MV |
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Admin @ si @ BEB2010 |
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1775 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Oriol Pujol; Jordi Vitria; Petia Radeva |
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Title |
On the Design of Low Redundancy Error-Correcting Output Codes |
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Book Chapter |
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2011 |
Publication |
Ensembles in Machine Learning Applications |
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373 |
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2 |
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21-38 |
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The classification of large number of object categories is a challenging trend in the Pattern Recognition field. In the literature, this is often addressed using an ensemble of classifiers . In this scope, the Error-Correcting Output Codes framework has demonstrated to be a powerful tool for combining classifiers. However, most of the state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a compact design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best compact ECOC code configuration. The results over several public UCI data sets and different multi-class Computer Vision problems show that the proposed methodology obtains comparable (even better) results than the state-of-the-art ECOC methodologies with far less number of dichotomizers. |
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Springer Berlin Heidelberg |
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1860-949X |
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978-3-642-22909-1 |
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MILAB; OR;HuPBA;MV |
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Admin @ si @ BEB2011b |
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1886 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Petia Radeva; Jordi Vitria; Oriol Pujol |
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Title |
Minimal Design of Error-Correcting Output Codes |
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Journal Article |
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2011 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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33 |
Issue |
6 |
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693-702 |
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Multi-class classification; Error-correcting output codes; Ensemble of classifiers |
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Abstract |
IF JCR CCIA 1.303 2009 54/103
The classification of large number of object categories is a challenging trend in the pattern recognition field. In literature, this is often addressed using an ensemble of classifiers. In this scope, the Error-correcting output codes framework has demonstrated to be a powerful tool for combining classifiers. However, most state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration. The results over several public UCI datasets and different multi-class computer vision problems show that the proposed methodology obtains comparable (even better) results than state-of-the-art ECOC methodologies with far less number of dichotomizers. |
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Elsevier |
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0167-8655 |
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MILAB; OR;HuPBA;MV |
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Admin @ si @ BEB2011a |
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1800 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Miguel Angel Bautista; Xavier Baro; Oriol Pujol; Petia Radeva; Jordi Vitria; Sergio Escalera |
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Title |
Compact Evolutive Design of Error-Correcting Output Codes |
Type |
Conference Article |
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2010 |
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Supervised and Unsupervised Ensemble Methods and their Applications in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases |
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119-128 |
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Ensemble of Dichotomizers; Error-Correcting Output Codes; Evolutionary optimization |
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The classication of large number of object categories is a challenging trend in the Machine Learning eld. In literature, this is often addressed using an ensemble of classiers. In this scope, the Error-Correcting Output Codes framework has demonstrated to be a powerful tool for the combination of classiers. However, most of the state-of-the-art ECOC approaches use a linear or exponential number of classiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classiers. Evolutionary computation is used for tuning the parameters of the classiers and looking for the best Minimal ECOC code conguration. The results over several public UCI data sets and a challenging multi-class Computer Vision problem show that the proposed methodology obtains comparable and even better results than state-of-the-art ECOC methodologies with far less number of dichotomizers. |
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Barcelona (Spain) |
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SUEMA |
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OR;MILAB;HUPBA;MV |
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BCNPCL @ bcnpcl @ BBP2010 |
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1363 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Miguel Oliveira; Angel Sappa; V. Santos |
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Title |
Color Correction using 3D Gaussian Mixture Models |
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Conference Article |
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2012 |
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9th International Conference on Image Analysis and Recognition |
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7324 |
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I |
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97-106 |
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The current paper proposes a novel color correction approach based on a probabilistic segmentation framework by using 3D Gaussian Mixture Models. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. The proposed approach is evaluated using both a recently published metric and two large data sets composed of seventy images. The evaluation is performed by comparing our algorithm with eight well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches. |
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Springer Berlin Heidelberg |
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0302-9743 |
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10.1007/978-3-642-31295-3_12 |
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ICIAR |
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ADAS |
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Admin @ si @ OSS2012a |
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2015 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Miguel Oliveira; Angel Sappa; V. Santos |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Color Correction for Onboard Multi-camera Systems using 3D Gaussian Mixture Models |
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Conference Article |
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2012 |
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IEEE Intelligent Vehicles Symposium |
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299-303 |
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The current paper proposes a novel color correction approach for onboard multi-camera systems. It works by segmenting the given images into several regions. A probabilistic segmentation framework, using 3D Gaussian Mixture Models, is proposed. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. An image data set of road scenarios is used to establish a performance comparison of the proposed method with other seven well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches. |
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Alcalá de Henares |
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IEEE Xplore |
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1931-0587 |
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978-1-4673-2119-8 |
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IV |
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Admin @ si @ OSS2012b |
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2021 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Miguel Oliveira; Angel Sappa; V.Santos |
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Title |
Unsupervised Local Color Correction for Coarsely Registered Images |
Type |
Conference Article |
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2011 |
Publication |
IEEE conference on Computer Vision and Pattern Recognition |
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201-208 |
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The current paper proposes a new parametric local color correction technique. Initially, several color transfer functions are computed from the output of the mean shift color segmentation algorithm. Secondly, color influence maps are calculated. Finally, the contribution of every color transfer function is merged using the weights from the color influence maps. The proposed approach is compared with both global and local color correction approaches. Results show that our method outperforms the technique ranked first in a recent performance evaluation on this topic. Moreover, the proposed approach is computed in about one tenth of the time. |
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Colorado Springs |
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1063-6919 |
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978-1-4577-0394-2 |
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Admin @ si @ OSS2011; ADAS @ adas @ |
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1766 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Miguel Oliveira; Angel Sappa; Victor Santos |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
A probabilistic approach for color correction in image mosaicking applications |
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Journal Article |
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2015 |
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IEEE Transactions on Image Processing |
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TIP |
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14 |
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2 |
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508 - 523 |
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Color correction; image mosaicking; color transfer; color palette mapping functions |
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Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. This paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image. An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets. Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures. |
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1057-7149 |
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ADAS; 600.076 |
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Admin @ si @ OSS2015b |
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2554 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Miguel Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel Sappa; A. Tom |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Concurrent Learning of Visual Codebooks and Object Categories in Openended Domains |
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2015 |
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International Conference on Intelligent Robots and Systems |
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2488 - 2495 |
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Visual Learning; Computer Vision; Autonomous Agents |
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In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using offline constructed codebooks. |
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Hamburg; Germany; October 2015 |
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ADAS; 600.076 |
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Admin @ si @ OSL2015 |
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2664 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Miguel Oliveira; V.Santos; Angel Sappa |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Short term path planning using a multiple hypothesis evaluation approach for an autonomous driving competition |
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2012 |
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IEEE 4th Workshop on Planning, Perception and Navigation for Intelligent Vehicles |
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Algarve; Portugal |
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Admin @ si @ OSS2012c |
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2159 |
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