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首页> 外文期刊>Neural processing letters >A Generative Probabilistic Oriented Wavelet Model for Texture Segmentation
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A Generative Probabilistic Oriented Wavelet Model for Texture Segmentation

机译：用于纹理分割的面向生成概率的小波模型

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摘要

This Letter addresses image segmentation via a generative model approach. A Bayesian network (BNT) in the space of dyadic wavelet transform coefficients is introduced to model texture images. The model is similar to a Hidden Markov model (HMM), but with non-stationary transitive conditional probability distributions. It is composed of discrete hidden variables and observable Gaussian outputs for wavelet coefficients. In particular, the Gabor wavelet transform is considered. The introduced model is compared with the simplest joint Gaussian probabilistic model for Gabor wavelet coefficients for several textures from the Brodatz album[1]. The comparison is based on cross-validation and includes probabilistic model ensembles instead of single models. In addition, the robustness of the models to cope with additive Gaussian noise is investigated. We further study the feasibility of the introduced generative model for image segmentation in the novelty detection framework [2]. Two examples are considered: (ⅰ) sea surface pollution detection from intensity images and (ⅱ) image segmentation of the still images with varying illumination across the scene.
机译：这封信通过生成模型方法解决了图像分割问题。引入二进小波变换系数空间中的贝叶斯网络（BNT）对纹理图像进行建模。该模型与隐马尔可夫模型（HMM）相似，但是具有非平稳的传递条件概率概率分布。它由离散的隐藏变量和可观察的高斯小波系数输出组成。特别地，考虑了Gabor小波变换。将引入的模型与最简单的联合高斯概率模型在Brodatz专辑中针对几种纹理的Gabor小波系数进行比较[1]。比较是基于交叉验证的，并且包括概率模型集合而不是单个模型。此外，还研究了模型应对加性高斯噪声的鲁棒性。我们将在新颖性检测框架中进一步研究引入的生成模型进行图像分割的可行性[2]。考虑了两个示例：（ⅰ）从强度图像检测海面污染，以及（ⅱ）在整个场景中光照变化的静态图像的图像分割。

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