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» Texture analysis using Renyi's generalized entropies
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KDD
2001
ACM
149views Data Mining» more  KDD 2001»
15 years 10 months ago
Maximum entropy methods for biological sequence modeling
Many of the same modeling methods used in natural languages, speci cally Markov models and HMM's, have also been applied to biological sequence analysis. In recent years, nat...
Eugen C. Buehler, Lyle H. Ungar
COLT
2000
Springer
15 years 2 months ago
Entropy Numbers of Linear Function Classes
This paper collects together a miscellany of results originally motivated by the analysis of the generalization performance of the “maximum-margin” algorithm due to Vapnik and...
Robert C. Williamson, Alex J. Smola, Bernhard Sch&...
ECCV
2006
Springer
15 years 11 months ago
Unsupervised Texture Segmentation with Nonparametric Neighborhood Statistics
Abstract. This paper presents a novel approach to unsupervised texture segmentation that relies on a very general nonparametric statistical model of image neighborhoods. The method...
Suyash P. Awate, Tolga Tasdizen, Ross T. Whitaker
CVPR
2000
IEEE
15 years 11 months ago
Learning in Gibbsian Fields: How Accurate and How Fast Can It Be?
?Gibbsian fields or Markov random fields are widely used in Bayesian image analysis, but learning Gibbs models is computationally expensive. The computational complexity is pronoun...
Song Chun Zhu, Xiuwen Liu
GECCO
2004
Springer
151views Optimization» more  GECCO 2004»
15 years 3 months ago
Discovery of Human-Competitive Image Texture Feature Extraction Programs Using Genetic Programming
In this paper we show how genetic programming can be used to discover useful texture feature extraction algorithms. Grey level histograms of different textures are used as inputs ...
Brian T. Lam, Victor Ciesielski