49,985 research outputs found
Relevance Subject Machine: A Novel Person Re-identification Framework
We propose a novel method called the Relevance Subject Machine (RSM) to solve
the person re-identification (re-id) problem. RSM falls under the category of
Bayesian sparse recovery algorithms and uses the sparse representation of the
input video under a pre-defined dictionary to identify the subject in the
video. Our approach focuses on the multi-shot re-id problem, which is the
prevalent problem in many video analytics applications. RSM captures the
essence of the multi-shot re-id problem by constraining the support of the
sparse codes for each input video frame to be the same. Our proposed approach
is also robust enough to deal with time varying outliers and occlusions by
introducing a sparse, non-stationary noise term in the model error. We provide
a novel Variational Bayesian based inference procedure along with an intuitive
interpretation of the proposed update rules. We evaluate our approach over
several commonly used re-id datasets and show superior performance over current
state-of-the-art algorithms. Specifically, for ILIDS-VID, a recent large scale
re-id dataset, RSM shows significant improvement over all published approaches,
achieving an 11.5% (absolute) improvement in rank 1 accuracy over the closest
competing algorithm considered.Comment: Submitted to IEEE Transactions on Pattern Analysis and Machine
Intelligenc
Sampling of graph signals with successive local aggregations
A new scheme to sample signals defined in the nodes of a graph is proposed.
The underlying assumption is that such signals admit a sparse representation in
a frequency domain related to the structure of the graph, which is captured by
the so-called graph-shift operator. Most of the works that have looked at this
problem have focused on using the value of the signal observed at a subset of
nodes to recover the signal in the entire graph. Differently, the sampling
scheme proposed here uses as input observations taken at a single node. The
observations correspond to sequential applications of the graph-shift operator,
which are linear combinations of the information gathered by the neighbors of
the node. When the graph corresponds to a directed cycle (which is the support
of time-varying signals), our method is equivalent to the classical sampling in
the time domain. When the graph is more general, we show that the Vandermonde
structure of the sampling matrix, which is critical to guarantee recovery when
sampling time-varying signals, is preserved. Sampling and interpolation are
analyzed first in the absence of noise and then noise is considered. We then
study the recovery of the sampled signal when the specific set of frequencies
that is active is not known. Moreover, we present a more general sampling
scheme, under which, either our aggregation approach or the alternative
approach of sampling a graph signal by observing the value of the signal at a
subset of nodes can be both viewed as particular cases. The last part of the
paper presents numerical experiments that illustrate the results developed
through both synthetic graph signals and a real-world graph of the economy of
the United States.Comment: Submitted to IEEE Transactions on Signal Processin
Modified Hard Thresholding Pursuit with Regularization Assisted Support Identification
Hard thresholding pursuit (HTP) is a recently proposed iterative sparse
recovery algorithm which is a result of combination of a support selection step
from iterated hard thresholding (IHT) and an estimation step from the
orthogonal matching pursuit (OMP). HTP has been seen to enjoy improved recovery
guarantee along with enhanced speed of convergence. Much of the success of HTP
can be attributed to its improved support selection capability due to the
support selection step from IHT. In this paper, we propose a generalized HTP
algorithm, called regularized HTP (RHTP), where the support selection step of
HTP is replaced by a IHT-type support selection where the cost function is
replaced by a regularized cost function, while the estimation step continues to
use the least squares function. With decomposable regularizer, satisfying
certain regularity conditions, the RHTP algorithm is shown to produce a
sequence dynamically equivalent to a sequence evolving according to a HTP-like
evolution, where the identification stage has a gradient premultiplied with a
time-varying diagonal matrix. RHTP is also proven, both theoretically, and
numerically, to enjoy faster convergence vis-a-vis HTP with both noiseless and
noisy measurement vectors.Comment: 10 pages, 5 figure
Robust Bayesian Method for Simultaneous Block Sparse Signal Recovery with Applications to Face Recognition
In this paper, we present a novel Bayesian approach to recover simultaneously
block sparse signals in the presence of outliers. The key advantage of our
proposed method is the ability to handle non-stationary outliers, i.e. outliers
which have time varying support. We validate our approach with empirical
results showing the superiority of the proposed method over competing
approaches in synthetic data experiments as well as the multiple measurement
face recognition problem.Comment: To appear in ICIP 201
Dynamic Filtering of Time-Varying Sparse Signals via l1 Minimization
Despite the importance of sparsity signal models and the increasing
prevalence of high-dimensional streaming data, there are relatively few
algorithms for dynamic filtering of time-varying sparse signals. Of the
existing algorithms, fewer still provide strong performance guarantees. This
paper examines two algorithms for dynamic filtering of sparse signals that are
based on efficient l1 optimization methods. We first present an analysis for
one simple algorithm (BPDN-DF) that works well when the system dynamics are
known exactly. We then introduce a novel second algorithm (RWL1-DF) that is
more computationally complex than BPDN-DF but performs better in practice,
especially in the case where the system dynamics model is inaccurate.
Robustness to model inaccuracy is achieved by using a hierarchical
probabilistic data model and propagating higher-order statistics from the
previous estimate (akin to Kalman filtering) in the sparse inference process.
We demonstrate the properties of these algorithms on both simulated data as
well as natural video sequences. Taken together, the algorithms presented in
this paper represent the first strong performance analysis of dynamic filtering
algorithms for time-varying sparse signals as well as state-of-the-art
performance in this emerging application.Comment: 26 pages, 8 figures. arXiv admin note: substantial text overlap with
arXiv:1208.032
Recursive Recovery of Sparse Signal Sequences from Compressive Measurements: A Review
In this article, we review the literature on design and analysis of recursive
algorithms for reconstructing a time sequence of sparse signals from
compressive measurements. The signals are assumed to be sparse in some
transform domain or in some dictionary. Their sparsity patterns can change with
time, although, in many practical applications, the changes are gradual. An
important class of applications where this problem occurs is dynamic projection
imaging, e.g., dynamic magnetic resonance imaging (MRI) for real-time medical
applications such as interventional radiology, or dynamic computed tomography.Comment: To appear in IEEE Trans. Signal Processin
Joint Channel Training and Feedback for FDD Massive MIMO Systems
Massive multiple-input multiple-output (MIMO) is widely recognized as a
promising technology for future 5G wireless communication systems. To achieve
the theoretical performance gains in massive MIMO systems, accurate channel
state information at the transmitter (CSIT) is crucial. Due to the overwhelming
pilot signaling and channel feedback overhead, however, conventional downlink
channel estimation and uplink channel feedback schemes might not be suitable
for frequency-division duplexing (FDD) massive MIMO systems. In addition, these
two topics are usually separately considered in the literature. In this paper,
we propose a joint channel training and feedback scheme for FDD massive MIMO
systems. Specifically, we firstly exploit the temporal correlation of
time-varying channels to propose a differential channel training and feedback
scheme, which simultaneously reduces the overhead for downlink training and
uplink feedback. We next propose a structured compressive sampling matching
pursuit (S-CoSaMP) algorithm to acquire a reliable CSIT by exploiting the
structured sparsity of wireless MIMO channels. Simulation results demonstrate
that the proposed scheme can achieve substantial reduction in the training and
feedback overhead
From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals
Conventional sub-Nyquist sampling methods for analog signals exploit prior
information about the spectral support. In this paper, we consider the
challenging problem of blind sub-Nyquist sampling of multiband signals, whose
unknown frequency support occupies only a small portion of a wide spectrum. Our
primary design goals are efficient hardware implementation and low
computational load on the supporting digital processing. We propose a system,
named the modulated wideband converter, which first multiplies the analog
signal by a bank of periodic waveforms. The product is then lowpass filtered
and sampled uniformly at a low rate, which is orders of magnitude smaller than
Nyquist. Perfect recovery from the proposed samples is achieved under certain
necessary and sufficient conditions. We also develop a digital architecture,
which allows either reconstruction of the analog input, or processing of any
band of interest at a low rate, that is, without interpolating to the high
Nyquist rate. Numerical simulations demonstrate many engineering aspects:
robustness to noise and mismodeling, potential hardware simplifications,
realtime performance for signals with time-varying support and stability to
quantization effects. We compare our system with two previous approaches:
periodic nonuniform sampling, which is bandwidth limited by existing hardware
devices, and the random demodulator, which is restricted to discrete multitone
signals and has a high computational load. In the broader context of Nyquist
sampling, our scheme has the potential to break through the bandwidth barrier
of state-of-the-art analog conversion technologies such as interleaved
converters.Comment: 17 pages, 12 figures, to appear in IEEE Journal of Selected Topics in
Signal Processing, the special issue on Compressed Sensin
IDENT: Identifying Differential Equations with Numerical Time evolution
Identifying unknown differential equations from a given set of discrete time
dependent data is a challenging problem. A small amount of noise can make the
recovery unstable, and nonlinearity and differential equations with varying
coefficients add complexity to the problem. We assume that the governing
partial differential equation (PDE) is a linear combination of a subset of a
prescribed dictionary containing different differential terms, and the
objective of this paper is to find the correct coefficients.
We propose a new direction based on the fundamental idea of convergence
analysis of numerical PDE schemes. We utilize Lasso for efficiency, and a
performance guarantee is established based on an incoherence property. The main
contribution is to validate and correct the results by Time Evolution Error
(TEE). The new algorithm, called Identifying Differential Equations with
Numerical Time evolution (IDENT), is explored for data with non-periodic
boundary conditions, noisy data and PDEs with varying coefficients. From the
recovery analysis of Lasso, we propose a new definition of Noise-to-Signal
ratio, which better represents the level of noise in the case of PDE
identification. We systematically analyze the effects of data generations and
downsampling, and propose an order preserving denoising method called
Least-Squares Moving Average (LSMA), to preprocess the given data. For the
identification of PDEs with varying coefficients, we propose to add Base
Element Expansion (BEE) to aide the computation. Various numerical experiments
from basic tests to noisy data, downsampling effects and varying coefficients
are presented
LAMP: A Locally Adapting Matching Pursuit Framework for Group Sparse Signatures in Ultra-Wide Band Radar Imaging
It has been found that radar returns of extended targets are not only sparse
but also exhibit a tendency to cluster into randomly located, variable sized
groups. However, the standard techniques of Compressive Sensing as applied in
radar imaging hardly considers the clustering tendency into account while
reconstructing the image from the compressed measurements. If the group
sparsity is taken into account, it is intuitive that one might obtain better
results both in terms of accuracy and time complexity as compared to the
conventional recovery techniques like Orthogonal Matching Pursuit (OMP). In
order to remedy this, techniques like Block OMP have been used in the existing
literature. An alternate approach is via reconstructing the signal by
transforming into the Hough Transform Domain where they become point-wise
sparse. However, these techniques essentially assume specific size and
structure of the groups and are not always effective if the exact
characteristics of the groups are not known, prior to reconstruction. In this
manuscript, a novel framework that we call locally adapting matching pursuit
(LAMP) have been proposed for efficient reconstruction of group sparse signals
from compressed measurements without assuming any specific size, location, or
structure of the groups. The recovery guarantee of the LAMP and its superiority
compared to the existing algorithms has been established with respect to
accuracy, time complexity and flexibility in group size. LAMP has been
successfully used on a real-world, experimental data set.Comment: 14 pages,22 figures, Draft to be submitted to journa
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