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首页> 外文会议>International Conference on Pattern Recognition Applications and Methods >An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation
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An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation

机译：空间变化参数估计的非均匀贝叶斯纹理模型

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摘要

In statistical model based texture feature extraction, features based on spatially varying parameters achieve higher discriminative performances compared to spatially constant parameters. In this paper we formulate a novel Bayesian framework which achieves texture characterization by spatially varying parameters based on Gaussian Markov random fields. The parameter estimation is carried out by Metropolis-Hastings algorithm. The distributions of estimated spatially varying parameters are then used as successful discriminant texture features in classification and segmentation. Results show that novel features outperform traditional Gaussian Markov random field texture features which use spatially constant parameters. These features capture both pixel spatial dependencies and structural properties of a texture giving improved texture features for effective texture classification and segmentation.
机译：在基于统计模型的纹理特征提取中，与空间恒定参数相比，基于空间变化参数的特征实现了更高的鉴别性能。在本文中，我们制定了一种新颖的贝叶斯框架，通过基于Gaussian Markov随机字段的空间变化参数来实现纹理特征。参数估计由Metropolis-Hastings算法执行。然后将估计的空间变化参数分布作为分类和分割中成功的判别纹理特征。结果表明，新颖的特点优于使用空间恒定参数的传统高斯马尔可夫随机场纹理特征。这些特征捕获像素空间依赖性和纹理的结构属性，为有效的纹理分类和分割提供了改进的纹理特征。

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