92,735 research outputs found
The chopthin algorithm for resampling
Resampling is a standard step in particle filters and more generally
sequential Monte Carlo methods. We present an algorithm, called chopthin, for
resampling weighted particles. In contrast to standard resampling methods the
algorithm does not produce a set of equally weighted particles; instead it
merely enforces an upper bound on the ratio between the weights. Simulation
studies show that the chopthin algorithm consistently outperforms standard
resampling methods. The algorithms chops up particles with large weight and
thins out particles with low weight, hence its name. It implicitly guarantees a
lower bound on the effective sample size. The algorithm can be implemented
efficiently, making it practically useful. We show that the expected
computational effort is linear in the number of particles. Implementations for
C++, R (on CRAN), Python and Matlab are available.Comment: 14 pages, 4 figure
Comparison of Resampling Schemes for Particle Filtering
This contribution is devoted to the comparison of various resampling
approaches that have been proposed in the literature on particle filtering. It
is first shown using simple arguments that the so-called residual and
stratified methods do yield an improvement over the basic multinomial
resampling approach. A simple counter-example showing that this property does
not hold true for systematic resampling is given. Finally, some results on the
large-sample behavior of the simple bootstrap filter algorithm are given. In
particular, a central limit theorem is established for the case where
resampling is performed using the residual approach
Boosting Image Forgery Detection using Resampling Features and Copy-move analysis
Realistic image forgeries involve a combination of splicing, resampling,
cloning, region removal and other methods. While resampling detection
algorithms are effective in detecting splicing and resampling, copy-move
detection algorithms excel in detecting cloning and region removal. In this
paper, we combine these complementary approaches in a way that boosts the
overall accuracy of image manipulation detection. We use the copy-move
detection method as a pre-filtering step and pass those images that are
classified as untampered to a deep learning based resampling detection
framework. Experimental results on various datasets including the 2017 NIST
Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and
tampered images shows that there is a consistent increase of 8%-10% in
detection rates, when copy-move algorithm is combined with different resampling
detection algorithms
Semi-independent resampling for particle filtering
Among Sequential Monte Carlo (SMC) methods,Sampling Importance Resampling
(SIR) algorithms are based on Importance Sampling (IS) and on some
resampling-based)rejuvenation algorithm which aims at fighting against weight
degeneracy. However %whichever the resampling technique used this mechanism
tends to be insufficient when applied to informative or high-dimensional
models. In this paper we revisit the rejuvenation mechanism and propose a class
of parameterized SIR-based solutions which enable to adjust the tradeoff
between computational cost and statistical performances
Negative association, ordering and convergence of resampling methods
We study convergence and convergence rates for resampling schemes. Our first
main result is a general consistency theorem based on the notion of negative
association, which is applied to establish the almost-sure weak convergence of
measures output from Kitagawa's (1996) stratified resampling method. Carpenter
et al's (1999) systematic resampling method is similar in structure but can
fail to converge depending on the order of the input samples. We introduce a
new resampling algorithm based on a stochastic rounding technique of Srinivasan
(2001), which shares some attractive properties of systematic resampling, but
which exhibits negative association and therefore converges irrespective of the
order of the input samples. We confirm a conjecture made by Kitagawa (1996)
that ordering input samples by their states in yields a faster
rate of convergence; we establish that when particles are ordered using the
Hilbert curve in , the variance of the resampling error is
under mild conditions, where
is the number of particles. We use these results to establish asymptotic
properties of particle algorithms based on resampling schemes that differ from
multinomial resampling.Comment: 54 pages, including 30 pages of supplementary materials (a typo in
Algorithm 1 has been corrected
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