28,055 research outputs found
Color image segmentation using a spatial k-means clustering algorithm
This paper details the implementation of a new adaptive technique for color-texture segmentation that is a generalization of the standard K-Means algorithm. The standard K-Means algorithm produces accurate segmentation results only when applied to images defined by homogenous regions with respect to texture and color since no local constraints are applied to impose spatial continuity. In addition, the initialization of the K-Means algorithm is problematic and usually the initial cluster centers are randomly picked. In this paper we detail the implementation of a novel technique to select the dominant colors from the input image using the information from the color histograms. The main contribution of this work is the generalization of the K-Means algorithm that includes the primary features that describe the color smoothness and texture complexity in the process of pixel assignment. The resulting color segmentation scheme has been applied to a large number of natural images and the experimental data indicates the robustness of the new developed segmentation algorithm
A hybrid sampler for Poisson-Kingman mixture models
This paper concerns the introduction of a new Markov Chain Monte Carlo scheme
for posterior sampling in Bayesian nonparametric mixture models with priors
that belong to the general Poisson-Kingman class. We present a novel compact
way of representing the infinite dimensional component of the model such that
while explicitly representing this infinite component it has less memory and
storage requirements than previous MCMC schemes. We describe comparative
simulation results demonstrating the efficacy of the proposed MCMC algorithm
against existing marginal and conditional MCMC samplers
Generalized Forward-Backward Splitting
This paper introduces the generalized forward-backward splitting algorithm
for minimizing convex functions of the form , where
has a Lipschitz-continuous gradient and the 's are simple in the sense
that their Moreau proximity operators are easy to compute. While the
forward-backward algorithm cannot deal with more than non-smooth
function, our method generalizes it to the case of arbitrary . Our method
makes an explicit use of the regularity of in the forward step, and the
proximity operators of the 's are applied in parallel in the backward
step. This allows the generalized forward backward to efficiently address an
important class of convex problems. We prove its convergence in infinite
dimension, and its robustness to errors on the computation of the proximity
operators and of the gradient of . Examples on inverse problems in imaging
demonstrate the advantage of the proposed methods in comparison to other
splitting algorithms.Comment: 24 pages, 4 figure
Accelerating Parallel Tempering: Quantile Tempering Algorithm (QuanTA)
Using MCMC to sample from a target distribution, on a
-dimensional state space can be a difficult and computationally expensive
problem. Particularly when the target exhibits multimodality, then the
traditional methods can fail to explore the entire state space and this results
in a bias sample output. Methods to overcome this issue include the parallel
tempering algorithm which utilises an augmented state space approach to help
the Markov chain traverse regions of low probability density and reach other
modes. This method suffers from the curse of dimensionality which dramatically
slows the transfer of mixing information from the auxiliary targets to the
target of interest as . This paper introduces a novel
prototype algorithm, QuanTA, that uses a Gaussian motivated transformation in
an attempt to accelerate the mixing through the temperature schedule of a
parallel tempering algorithm. This new algorithm is accompanied by a
comprehensive theoretical analysis quantifying the improved efficiency and
scalability of the approach; concluding that under weak regularity conditions
the new approach gives accelerated mixing through the temperature schedule.
Empirical evidence of the effectiveness of this new algorithm is illustrated on
canonical examples
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