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Bogdan Raducanu; Fadi Dornaika |
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Title |
A Supervised Non-linear Dimensionality Reduction Approach for Manifold Learning |
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Journal Article |
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2012 |
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Pattern Recognition |
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PR |
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45 |
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6 |
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2432-2444 |
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Abstract  |
IF= 2.61
IF=2.61 (2010)
In this paper we introduce a novel supervised manifold learning technique called Supervised Laplacian Eigenmaps (S-LE), which makes use of class label information to guide the procedure of non-linear dimensionality reduction by adopting the large margin concept. The graph Laplacian is split into two components: within-class graph and between-class graph to better characterize the discriminant property of the data. Our approach has two important characteristics: (i) it adaptively estimates the local neighborhood surrounding each sample based on data density and similarity and (ii) the objective function simultaneously maximizes the local margin between heterogeneous samples and pushes the homogeneous samples closer to each other.
Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques, demonstrating its superiority. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variations in their appearance (such as hand or body pose, for instance. |
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Elsevier |
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0031-3203 |
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OR; MV |
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Admin @ si @ RaD2012a |
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1884 |
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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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Year |
2012 |
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Sensors |
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SENS |
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12 |
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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 |
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1885 |
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Author |
Agata Lapedriza; David Masip; Jordi Vitria |
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Title |
On the Use of External Face Features for Identity Verification |
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2006 |
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Journal of Multimedia, 1(4): 11–20 |
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1 |
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4 |
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11-20 |
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Face Verification, Computer Vision, Machine Learning |
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In general automatic face classification applications images are captured in natural environments. In these cases, the performance is affected by variations in facial images related to illumination, pose, occlusion or expressions. Most of the existing face classification systems use only the internal features information, composed by eyes, nose and mouth, since they are more difficult to imitate. Nevertheless, nowadays a lot of applications not related to security are developed, and in these cases the information located at head, chin or ears zones (external features) can be useful to improve the current accuracies. However, the lack of a natural alignment in these areas makes difficult to extract these features applying classic Bottom-Up methods. In this paper, we propose a complete scheme based on a Top-Down reconstruction algorithm to extract external features of face images. To test our system we have performed face verification experiments using public databases, given that identity verification is a general task that has many real life applications. We have considered images uniformly illuminated, images with occlusions and images with high local changes in the illumination, and the obtained results show that the information contributed by the external features can be useful for verification purposes, specially significant when faces are partially occluded. |
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OR;MV |
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BCNPCL @ bcnpcl @ LMV2006b |
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708 |
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Author |
Cesar Isaza; Joaquin Salas; Bogdan Raducanu |
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Title |
Rendering ground truth data sets to detect shadows cast by static objects in outdoors |
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2014 |
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Multimedia Tools and Applications |
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MTAP |
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70 |
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1 |
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557-571 |
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Synthetic ground truth data set; Sun position; Shadow detection; Static objects shadow detection |
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In our work, we are particularly interested in studying the shadows cast by static objects in outdoor environments, during daytime. To assess the accuracy of a shadow detection algorithm, we need ground truth information. The collection of such information is a very tedious task because it is a process that requires manual annotation. To overcome this severe limitation, we propose in this paper a methodology to automatically render ground truth using a virtual environment. To increase the degree of realism and usefulness of the simulated environment, we incorporate in the scenario the precise longitude, latitude and elevation of the actual location of the object, as well as the sun’s position for a given time and day. To evaluate our method, we consider a qualitative and a quantitative comparison. In the quantitative one, we analyze the shadow cast by a real object in a particular geographical location and its corresponding rendered model. To evaluate qualitatively the methodology, we use some ground truth images obtained both manually and automatically. |
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Springer US |
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1380-7501 |
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OR;MV |
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Admin @ si @ ISR2014 |
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2229 |
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Author |
L. Calvet; A. Ferrer; M. Gomes; A. Juan; David Masip |
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Title |
Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation |
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Journal Article |
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2016 |
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Computers & Industrial Engineering |
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CIE |
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94 |
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93-104 |
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Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning |
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In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial o er and customers show di erent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that di erent customer-depot assignment maps will lead to di erent customer-expenditure levels. As a consequence, market-segmentation strategiesneed to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here di ers in terms of the proposed solutions from the traditional one. |
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PERGAMON-ELSEVIER SCIENCE LTD |
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CIE |
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0360-8352 |
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OR;MV; |
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Admin @ si @ CFG2016 |
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2749 |
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