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Author |
Santiago Segui; Laura Igual; Jordi Vitria |
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Title |
Bagged One Class Classifiers in the Presence of Outliers |
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Journal Article |
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Year |
2013 |
Publication |
International Journal of Pattern Recognition and Artificial Intelligence |
Abbreviated Journal |
IJPRAI |
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27 |
Issue |
5 |
Pages |
1350014-1350035 |
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Keywords |
One-class Classifier; Ensemble Methods; Bagging and Outliers |
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Abstract |
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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no |
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Admin @ si @ SIV2013 |
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2256 |
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Author |
Laura Igual; Agata Lapedriza; Ricard Borras |
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Title |
Robust Gait-Based Gender Classification using Depth Cameras |
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Journal Article |
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Year |
2013 |
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EURASIP Journal on Advances in Signal Processing |
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EURASIPJ |
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37 |
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1 |
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72-80 |
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This article presents a new approach for gait-based gender recognition using depth cameras, that can run in real time. The main contribution of this study is a new fast feature extraction strategy that uses the 3D point cloud obtained from the frames in a gait cycle. For each frame, these points are aligned according to their centroid and grouped. After that, they are projected into their PCA plane, obtaining a representation of the cycle particularly robust against view changes. Then, final discriminative features are computed by first making a histogram of the projected points and then using linear discriminant analysis. To test the method we have used the DGait database, which is currently the only publicly available database for gait analysis that includes depth information. We have performed experiments on manually labeled cycles and over whole video sequences, and the results show that our method improves the accuracy significantly, compared with state-of-the-art systems which do not use depth information. Furthermore, our approach is insensitive to illumination changes, given that it discards the RGB information. That makes the method especially suitable for real applications, as illustrated in the last part of the experiments section. |
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MILAB; OR;MV |
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Admin @ si @ ILB2013 |
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2144 |
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Cesar Isaza; Joaquin Salas; Bogdan Raducanu |
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Title |
Evaluation of Intrinsic Image Algorithms to Detect the Shadows Cast by Static Objects Outdoors |
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2012 |
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Sensors |
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SENS |
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12 |
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10 |
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13333-13348 |
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In some automatic scene analysis applications, the presence of shadows becomes a nuisance that is necessary to deal with. As a consequence, a preliminary stage in many computer vision algorithms is to attenuate their effect. In this paper, we focus our attention on the detection of shadows cast by static objects outdoors, as the scene is viewed for extended periods of time (days, weeks) from a fixed camera and considering daylight intervals where the main source of light is the sun. In this context, we report two contributions. First, we introduce the use of synthetic images for which ground truth can be generated automatically, avoiding the tedious effort of manual annotation. Secondly, we report a novel application of the intrinsic image concept to the automatic detection of shadows cast by static objects in outdoors. We make both a quantitative and a qualitative evaluation of several algorithms based on this image representation. For the quantitative evaluation, we used the synthetic data set, while for the qualitative evaluation we used both data sets. Our experimental results show that the evaluated methods can partially solve the problem of shadow detection. |
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OR;MV |
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no |
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Admin @ si @ ISR2012b |
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2173 |
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Author |
Fadi Dornaika; Abdelmalik Moujahid; Bogdan Raducanu |
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Title |
Facial expression recognition using tracked facial actions: Classifier performance analysis |
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Journal Article |
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Year |
2013 |
Publication |
Engineering Applications of Artificial Intelligence |
Abbreviated Journal |
EAAI |
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26 |
Issue |
1 |
Pages |
467-477 |
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Visual face tracking; 3D deformable models; Facial actions; Dynamic facial expression recognition; Human–computer interaction |
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In this paper, we address the analysis and recognition of facial expressions in continuous videos. More precisely, we study classifiers performance that exploit head pose independent temporal facial action parameters. These are provided by an appearance-based 3D face tracker that simultaneously provides the 3D head pose and facial actions. The use of such tracker makes the recognition pose- and texture-independent. Two different schemes are studied. The first scheme adopts a dynamic time warping technique for recognizing expressions where training data are given by temporal signatures associated with different universal facial expressions. The second scheme models temporal signatures associated with facial actions with fixed length feature vectors (observations), and uses some machine learning algorithms in order to recognize the displayed expression. Experiments quantified the performance of different schemes. These were carried out on CMU video sequences and home-made video sequences. The results show that the use of dimension reduction techniques on the extracted time series can improve the classification performance. Moreover, these experiments show that the best recognition rate can be above 90%. |
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Elsevier |
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OR; 600.046;MV |
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no |
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Admin @ si @ DMR2013 |
Serial |
2185 |
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Author |
Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu |
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Title |
Estado del Arte en Sistemas de Vision Artificial para Personas Invidentes |
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Year |
2013 |
Publication |
Komputer Sapiens |
Abbreviated Journal |
KS |
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1 |
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20-25 |
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OR;MV |
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Admin @ si @ TSR2013 |
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2231 |
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