Toward Global Solution to MAP Image Estimation: Using Common Structure of Local Solutions

@inproceedings{Li1997TowardGS,
  title={Toward Global Solution to MAP Image Estimation: Using Common Structure of Local Solutions},
  author={S. Li},
  booktitle={EMMCVPR},
  year={1997}
}
  • S. Li
  • Published in EMMCVPR 21 May 1997
  • Computer Science
The maximum a posteriori (MAP) principle is often used in image restoration and segmentation to define the optimal solution when both the prior and likelihood distributions are available. MAP estimation is equivalent to minimizing an energy function. It is desirable to find the global minimum. However, the minimization in the MAP image estimation is non-trivial due to the use of contextual constraints between pixels. Steepest descent methods such as ICM quickly finds a local minimum but the… 
ACM Attributed Graph Clustering for Learning Classes of Images
TLDR
This paper modifications the incremental method for obtaining a prototypical graph to update these pdf's provided that they are statistically compatible with those of the corresponding nodes and edges, and integrates this approach in a EM clustering algorithm.
Recognizing Indoor Images with Unsupervised Segmentation and Graph Matching
TLDR
Both segmentation and matching results are presented that support the initial claim indicating that such an strategy provides both class discrimination and individual-within-a-class discrimination in indoor images which usually exhibit a high degree of perceptual ambiguity.
EM Algorithm for Clustering an Ensemble of Graphs with Comb Matching
TLDR
An improvement of the Comb algorithm for graph matching, a population-based method which performs multi-point explorations of the discrete space of feasible solutions, is used for unsupervised clustering of an ensemble of graphs the Asymmetric Clustering Model.
3 Memetic Algorithms
TLDR
Back in the late 60s and early 70s, several researchers laid the foundations of what the authors now know as Evolutionary Algorithms (EAs) and despite some hard beginnings, most researchers interested in search or optimization have grown to know and accept the existence – and indeed the usefulness – of these techniques.
Memetic Algorithms 1.1 Introduction
  • History
Back in the late 60s and early 70s, several researchers laid the foundations of what we now know as evolutionary algorithms [75, 108, 218, 227] (EAs). In these almost four decades, and despite some
Structural Recognition with Kernelized Softassign
TLDR
The results show that the kernelized version of the classical Softassign method, consisting on applying graph kernel engineering to matching problems, has a practical use for image classification in terms of pure structural information.
From Genes to Memes: Optimization by Problem-aware Evolutionary Algorithms
TLDR
The rationale behind this optimization philosophy, namely the intrinsic theoretical limitations of problem-unaware optimization techniques, is presented in this work.
Protein classification by matching and clustering surface graphs

References

SHOWING 1-10 OF 18 REFERENCES
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
  • S. Geman, D. Geman
  • Physics
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 1984
TLDR
The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Adaptive image segmentation using a genetic algorithm
TLDR
The adaptive image segmentation system is presented, which incorporates a genetic algorithm to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions such as time of day, time of year, clouds, etc.
Markov Random Field Modeling in Computer Vision
  • S. Li
  • Computer Science
    Computer Science Workbench
  • 1995
TLDR
This book presents a comprehensive study on the use of MRFs for solving computer vision problems, and covers the following parts essential to the subject: introduction to fundamental theories, formulations of MRF vision models, MRF parameter estimation, and optimization algorithms.
MAP image restoration and segmentation by constrained optimization
  • S. Li
  • Engineering
    IEEE Trans. Image Process.
  • 1998
The combinatorial optimization problem of MAP estimation is converted to one of constrained real optimization and then solved by using the augmented Lagrange-Hopfield (ALH) method proposed in 1984.
Optic Flow Field Segmentation and Motion Estimation Using a Robust Genetic Partitioning Algorithm
TLDR
A genetic partitioning algorithm is proposed that elegantly combines the robust estimation with the genetic algorithm by a bridging genetic operator called self-adaptation to solve the problem of segmenting an optic flow field consisting of a large portion of incorrect data or multiple motion groups.
On the Statistical Analysis of Dirty Pictures
may 7th, 1986, Professor A. F. M. Smith in the Chair] SUMMARY A continuous two-dimensional region is partitioned into a fine rectangular array of sites or "pixels", each pixel having a particular
Geometric Primitive Extraction Using a Genetic Algorithm
  • G. Roth, M. Levine
  • Mathematics, Computer Science
    IEEE Trans. Pattern Anal. Mach. Intell.
  • 1994
TLDR
A genetic algorithm based on a minimal subset representation of a geometric primitive is used to perform primitive extraction, capable of extracting more complex primitives than the Hough transform.
Optimization by Simulated Annealing
TLDR
A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems.
Genetic Algorithms in Search Optimization and Machine Learning
TLDR
This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Formal Memetic Algorithms
TLDR
A formal, representation-independent form of a memetic algorithm—a genetic algorithm incorporating local search—is introduced, and the memetic algorithms performed very well on the travelling sales-rep problem.
...
...