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Author |
Sergio Escalera; Xavier Baro; Jordi Vitria; Petia Radeva; Bogdan Raducanu |
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Title |
Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction |
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Journal Article |
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2012 |
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Sensors |
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SENS |
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Volume |
12 |
Issue |
2 |
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1702-1719 |
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Abstract |
IF=1.77 (2010)
Social interactions are a very important component in peopleís lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Timesí Blogging Heads opinion blog.
The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The linksí weights are a measure of the ìinfluenceî a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network. |
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Molecular Diversity Preservation International |
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MILAB; OR;HuPBA;MV |
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Admin @ si @ EBV2012 |
Serial |
1885 |
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Author |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
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Title |
Towards Automatic Polyp Detection with a Polyp Appearance Model |
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Journal Article |
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Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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45 |
Issue |
9 |
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3166-3182 |
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Colonoscopy,PolypDetection,RegionSegmentation,SA-DOVA descriptot |
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This work aims at the automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside. |
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Elsevier |
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0031-3203 |
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800 |
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IbPRIA |
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MV;SIAI |
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Admin @ si @ BSV2012; IAM @ iam |
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1997 |
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Author |
Santiago Segui; Michal Drozdzal; Fernando Vilariño; Carolina Malagelada; Fernando Azpiroz; Petia Radeva; Jordi Vitria |
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Title |
Categorization and Segmentation of Intestinal Content Frames for Wireless Capsule Endoscopy |
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2012 |
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IEEE Transactions on Information Technology in Biomedicine |
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TITB |
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16 |
Issue |
6 |
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1341-1352 |
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Wireless capsule endoscopy (WCE) is a device that allows the direct visualization of gastrointestinal tract with minimal discomfort for the patient, but at the price of a large amount of time for screening. In order to reduce this time, several works have proposed to automatically remove all the frames showing intestinal content. These methods label frames as {intestinal content – clear} without discriminating between types of content (with different physiological meaning) or the portion of image covered. In addition, since the presence of intestinal content has been identified as an indicator of intestinal motility, its accurate quantification can show a potential clinical relevance. In this paper, we present a method for the robust detection and segmentation of intestinal content in WCE images, together with its further discrimination between turbid liquid and bubbles. Our proposal is based on a twofold system. First, frames presenting intestinal content are detected by a support vector machine classifier using color and textural information. Second, intestinal content frames are segmented into {turbid, bubbles, and clear} regions. We show a detailed validation using a large dataset. Our system outperforms previous methods and, for the first time, discriminates between turbid from bubbles media. |
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1089-7771 |
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800 |
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MILAB; MV; OR;SIAI |
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no |
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Call Number |
Admin @ si @ SDV2012 |
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2124 |
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Author |
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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Journal Article |
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Year |
2012 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
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12 |
Issue |
10 |
Pages |
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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Admin @ si @ ISR2012b |
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2173 |
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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 |
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IJPRAI |
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Volume |
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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Admin @ si @ SIV2013 |
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2256 |
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