21,721 research outputs found
Automatic Analysis of Facial Expressions Based on Deep Covariance Trajectories
In this paper, we propose a new approach for facial expression recognition
using deep covariance descriptors. The solution is based on the idea of
encoding local and global Deep Convolutional Neural Network (DCNN) features
extracted from still images, in compact local and global covariance
descriptors. The space geometry of the covariance matrices is that of Symmetric
Positive Definite (SPD) matrices. By conducting the classification of static
facial expressions using Support Vector Machine (SVM) with a valid Gaussian
kernel on the SPD manifold, we show that deep covariance descriptors are more
effective than the standard classification with fully connected layers and
softmax. Besides, we propose a completely new and original solution to model
the temporal dynamic of facial expressions as deep trajectories on the SPD
manifold. As an extension of the classification pipeline of covariance
descriptors, we apply SVM with valid positive definite kernels derived from
global alignment for deep covariance trajectories classification. By performing
extensive experiments on the Oulu-CASIA, CK+, and SFEW datasets, we show that
both the proposed static and dynamic approaches achieve state-of-the-art
performance for facial expression recognition outperforming many recent
approaches.Comment: A preliminary version of this work appeared in "Otberdout N, Kacem A,
Daoudi M, Ballihi L, Berretti S. Deep Covariance Descriptors for Facial
Expression Recognition, in British Machine Vision Conference 2018, BMVC 2018,
Northumbria University, Newcastle, UK, September 3-6, 2018. ; 2018 :159."
arXiv admin note: substantial text overlap with arXiv:1805.0386
Towards Effective Codebookless Model for Image Classification
The bag-of-features (BoF) model for image classification has been thoroughly
studied over the last decade. Different from the widely used BoF methods which
modeled images with a pre-trained codebook, the alternative codebook free image
modeling method, which we call Codebookless Model (CLM), attracted little
attention. In this paper, we present an effective CLM that represents an image
with a single Gaussian for classification. By embedding Gaussian manifold into
a vector space, we show that the simple incorporation of our CLM into a linear
classifier achieves very competitive accuracy compared with state-of-the-art
BoF methods (e.g., Fisher Vector). Since our CLM lies in a high dimensional
Riemannian manifold, we further propose a joint learning method of low-rank
transformation with support vector machine (SVM) classifier on the Gaussian
manifold, in order to reduce computational and storage cost. To study and
alleviate the side effect of background clutter on our CLM, we also present a
simple yet effective partial background removal method based on saliency
detection. Experiments are extensively conducted on eight widely used databases
to demonstrate the effectiveness and efficiency of our CLM method
Tracking-Based Non-Parametric Background-Foreground Classification in a Chromaticity-Gradient Space
This work presents a novel background-foreground classification technique based on adaptive non-parametric kernel estimation in a color-gradient space of components. By combining normalized color components with their gradients, shadows are efficiently suppressed from the results, while the luminance information in the moving objects is preserved. Moreover, a fast multi-region iterative tracking strategy applied over previously detected foreground regions allows to construct a robust foreground modeling, which combined with the background model increases noticeably the quality in the detections. The proposed strategy has been applied to different kind of sequences, obtaining satisfactory results in complex situations such as those given by dynamic backgrounds, illumination changes, shadows and multiple moving objects
Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences
This paper introduces sparse coding and dictionary learning for Symmetric
Positive Definite (SPD) matrices, which are often used in machine learning,
computer vision and related areas. Unlike traditional sparse coding schemes
that work in vector spaces, in this paper we discuss how SPD matrices can be
described by sparse combination of dictionary atoms, where the atoms are also
SPD matrices. We propose to seek sparse coding by embedding the space of SPD
matrices into Hilbert spaces through two types of Bregman matrix divergences.
This not only leads to an efficient way of performing sparse coding, but also
an online and iterative scheme for dictionary learning. We apply the proposed
methods to several computer vision tasks where images are represented by region
covariance matrices. Our proposed algorithms outperform state-of-the-art
methods on a wide range of classification tasks, including face recognition,
action recognition, material classification and texture categorization
Learning Discriminative Stein Kernel for SPD Matrices and Its Applications
Stein kernel has recently shown promising performance on classifying images
represented by symmetric positive definite (SPD) matrices. It evaluates the
similarity between two SPD matrices through their eigenvalues. In this paper,
we argue that directly using the original eigenvalues may be problematic
because: i) Eigenvalue estimation becomes biased when the number of samples is
inadequate, which may lead to unreliable kernel evaluation; ii) More
importantly, eigenvalues only reflect the property of an individual SPD matrix.
They are not necessarily optimal for computing Stein kernel when the goal is to
discriminate different sets of SPD matrices. To address the two issues in one
shot, we propose a discriminative Stein kernel, in which an extra parameter
vector is defined to adjust the eigenvalues of the input SPD matrices. The
optimal parameter values are sought by optimizing a proxy of classification
performance. To show the generality of the proposed method, three different
kernel learning criteria that are commonly used in the literature are employed
respectively as a proxy. A comprehensive experimental study is conducted on a
variety of image classification tasks to compare our proposed discriminative
Stein kernel with the original Stein kernel and other commonly used methods for
evaluating the similarity between SPD matrices. The experimental results
demonstrate that, the discriminative Stein kernel can attain greater
discrimination and better align with classification tasks by altering the
eigenvalues. This makes it produce higher classification performance than the
original Stein kernel and other commonly used methods.Comment: 13 page
Kernel methods for detecting coherent structures in dynamical data
We illustrate relationships between classical kernel-based dimensionality
reduction techniques and eigendecompositions of empirical estimates of
reproducing kernel Hilbert space (RKHS) operators associated with dynamical
systems. In particular, we show that kernel canonical correlation analysis
(CCA) can be interpreted in terms of kernel transfer operators and that it can
be obtained by optimizing the variational approach for Markov processes (VAMP)
score. As a result, we show that coherent sets of particle trajectories can be
computed by kernel CCA. We demonstrate the efficiency of this approach with
several examples, namely the well-known Bickley jet, ocean drifter data, and a
molecular dynamics problem with a time-dependent potential. Finally, we propose
a straightforward generalization of dynamic mode decomposition (DMD) called
coherent mode decomposition (CMD). Our results provide a generic machine
learning approach to the computation of coherent sets with an objective score
that can be used for cross-validation and the comparison of different methods
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