540 research outputs found
Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics
Kernel-based methods exhibit well-documented performance in various nonlinear
learning tasks. Most of them rely on a preselected kernel, whose prudent choice
presumes task-specific prior information. Especially when the latter is not
available, multi-kernel learning has gained popularity thanks to its
flexibility in choosing kernels from a prescribed kernel dictionary. Leveraging
the random feature approximation and its recent orthogonality-promoting
variant, the present contribution develops a scalable multi-kernel learning
scheme (termed Raker) to obtain the sought nonlinear learning function `on the
fly,' first for static environments. To further boost performance in dynamic
environments, an adaptive multi-kernel learning scheme (termed AdaRaker) is
developed. AdaRaker accounts not only for data-driven learning of kernel
combination, but also for the unknown dynamics. Performance is analyzed in
terms of both static and dynamic regrets. AdaRaker is uniquely capable of
tracking nonlinear learning functions in environments with unknown dynamics,
and with with analytic performance guarantees. Tests with synthetic and real
datasets are carried out to showcase the effectiveness of the novel algorithms.Comment: 36 page
Facial Action Recognition Combining Heterogeneous Features via Multi-Kernel Learning
International audienceThis paper presents our response to the first interna- tional challenge on Facial Emotion Recognition and Analysis. We propose to combine different types of features to automatically detect Action Units in facial images. We use one multi-kernel SVM for each Action Unit we want to detect. The first kernel matrix is computed using Local Gabor Binary Pattern histograms and a histogram intersection kernel. The second kernel matrix is computed from AAM coefficients and an RBF kernel. During the training step, we combine these two types of features using the recently proposed SimpleMKL algorithm. SVM outputs are then averaged to exploit temporal information in the sequence. To eval- uate our system, we perform deep experimentations on several key issues: influence of features and kernel function in histogram- based SVM approaches, influence of spatially-independent in- formation versus geometric local appearance information and benefits of combining both, sensitivity to training data and interest of temporal context adaptation. We also compare our results to those of the other participants and try to explain why our method had the best performance during the FERA challenge
Human Action Recognition using Multi-Kernel Learning for Temporal Residual Network
This paper has been presented at the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.Deep learning has led to a series of breakthrough in the human action recognition field. Given the powerful representational ability of residual networks (ResNet), performance in many computer vision tasks including human action recognition has improved. Motivated by the success of ResNet, we use the residual network and its variations to obtain feature representation. Bearing in mind the importance of appearance and motion information for action representation, our network utilizes both for feature extraction. Appearance and motion features are further fused for action classification using a multi-kernel support vector machine (SVM).We also investigate the fusion of dense trajectories with the proposed network to boost up the network performance. We evaluate our proposed methods on a benchmark dataset (HMDB-51) and results shows the multi-kernel learning shows the better performance than the fusion of classification score from deep network SoftMax layer. Our proposed method also shows good performance as compared to the recent state-of-the-art methods.Sergio A. Velastin has received funding from the Universidad Carlos III de Madrid, the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement nâ—¦ 600371, el Ministerio de EconomÃa, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander. Authors also acknowledge support from the Higher Education Commission, Pakistan
A deep representation for depth images from synthetic data
Convolutional Neural Networks (CNNs) trained on large scale RGB databases
have become the secret sauce in the majority of recent approaches for object
categorization from RGB-D data. Thanks to colorization techniques, these
methods exploit the filters learned from 2D images to extract meaningful
representations in 2.5D. Still, the perceptual signature of these two kind of
images is very different, with the first usually strongly characterized by
textures, and the second mostly by silhouettes of objects. Ideally, one would
like to have two CNNs, one for RGB and one for depth, each trained on a
suitable data collection, able to capture the perceptual properties of each
channel for the task at hand. This has not been possible so far, due to the
lack of a suitable depth database. This paper addresses this issue, proposing
to opt for synthetically generated images rather than collecting by hand a 2.5D
large scale database. While being clearly a proxy for real data, synthetic
images allow to trade quality for quantity, making it possible to generate a
virtually infinite amount of data. We show that the filters learned from such
data collection, using the very same architecture typically used on visual
data, learns very different filters, resulting in depth features (a) able to
better characterize the different facets of depth images, and (b) complementary
with respect to those derived from CNNs pre-trained on 2D datasets. Experiments
on two publicly available databases show the power of our approach
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