6,554 research outputs found
Context guided belief propagation for remote sensing image classification.
We propose a context guided belief propagation (BP) algorithm to perform high spatial resolution multispectral imagery (HSRMI) classification efficiently utilizing superpixel representation. One important characteristic of HSRMI is that different land cover objects possess a similar spectral property. This property is exploited to speed up the standard BP (SBP) in the classification process. Specifically, we leverage this property of HSRMI as context information to guide messages passing in SBP. Furthermore, the spectral and structural features extracted at the superpixel level are fed into a Markov random field framework to address the challenge of low interclass variation in HSRMI classification by minimizing the discrete energy through context guided BP (CBP). Experiments show that the proposed CBP is significantly faster than the SBP while retaining similar performance as compared with SBP. Compared to the baseline methods, higher classification accuracy is achieved by the proposed CBP when the context information is used with both spectral and structural features
Block Iterative Eigensolvers for Sequences of Correlated Eigenvalue Problems
In Density Functional Theory simulations based on the LAPW method, each
self-consistent field cycle comprises dozens of large dense generalized
eigenproblems. In contrast to real-space methods, eigenpairs solving for
problems at distinct cycles have either been believed to be independent or at
most very loosely connected. In a recent study [7], it was demonstrated that,
contrary to belief, successive eigenproblems in a sequence are strongly
correlated with one another. In particular, by monitoring the subspace angles
between eigenvectors of successive eigenproblems, it was shown that these
angles decrease noticeably after the first few iterations and become close to
collinear. This last result suggests that we can manipulate the eigenvectors,
solving for a specific eigenproblem in a sequence, as an approximate solution
for the following eigenproblem. In this work we present results that are in
line with this intuition. We provide numerical examples where opportunely
selected block iterative eigensolvers benefit from the reuse of eigenvectors by
achieving a substantial speed-up. The results presented will eventually open
the way to a widespread use of block iterative eigensolvers in ab initio
electronic structure codes based on the LAPW approach.Comment: 12 Pages, 5 figures. Accepted for publication on Computer Physics
Communication
Gaussian Belief with dynamic data and in dynamic network
In this paper we analyse Belief Propagation over a Gaussian model in a
dynamic environment. Recently, this has been proposed as a method to average
local measurement values by a distributed protocol ("Consensus Propagation",
Moallemi & Van Roy, 2006), where the average is available for read-out at every
single node. In the case that the underlying network is constant but the values
to be averaged fluctuate ("dynamic data"), convergence and accuracy are
determined by the spectral properties of an associated Ruelle-Perron-Frobenius
operator. For Gaussian models on Erdos-Renyi graphs, numerical computation
points to a spectral gap remaining in the large-size limit, implying
exceptionally good scalability. In a model where the underlying network also
fluctuates ("dynamic network"), averaging is more effective than in the dynamic
data case. Altogether, this implies very good performance of these methods in
very large systems, and opens a new field of statistical physics of large (and
dynamic) information systems.Comment: 5 pages, 7 figure
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