Unsupervised learning of a finite discrete mixture: Applications to texture modeling and image databases summarization

Nizar Bouguila; Djemel Ziou

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Unsupervised learning of a finite discrete mixture: Applications to texture modeling and image databases summarization

机译：有限离散混合的无监督学习：在纹理建模和图像数据库摘要中的应用

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摘要

This paper presents an unsupervised learning algorithm for fitting a finite mixture model based on the Multinomial Dirichlet distribution (MDD). This mixture is particularly useful for modeling discrete data (vectors of counts). The algorithm proposed is based on the expectation maximization (EM) approach. This mixture is used to improve image databases categorization by integrating semantic features and to produce a new texture model. For the texture modeling problem, the results are reported on the Vistex texture image database from the MIT Media Lab.

机译：本文提出了一种基于多项式Dirichlet分布（MDD）的有限混合模型拟合的无监督学习算法。这种混合对于建模离散数据（计数向量）特别有用。提出的算法基于期望最大化（EM）方法。这种混合用于通过集成语义特征来改进图像数据库的分类并生成新的纹理模型。对于纹理建模问题，结果在MIT媒体实验室的Vistex纹理图像数据库中报告。

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来源
《Journal of visual communication & image representation》 |2007年第4期|295-309|共15页
作者
Nizar Bouguila; Djemel Ziou;
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作者单位

Concordia Institute for Information Systems Engineering, Faculty of Engineering and Computer Science, Concordia University, Montreal, Que., Canada H3G 2W1;

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原文格式 PDF
正文语种 eng
中图分类无线电电子学、电信技术;
关键词
multinomial dirichlet; finite mixture models; maximum likelihood; EM; semantic features; image retrieval; vistex; cooccurrence matrix;

机译：多项式狄利克雷有限混合模型;最大似然;EM;语义特征;图像检索;粘胶共现矩阵;

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1. Unsupervised Learning of Generalized Gamma Mixture Model With Application in Statistical Modeling of High-Resolution SAR Images [J] . H. C. Li, V. A. Krylov, P. Z. Fan, IEEE Transactions on Geoscience and Remote Sensing . 2016,第4期

机译：广义伽玛混合模型的无监督学习及其在高分辨率SAR图像统计建模中的应用
2. Using unsupervised learning of a finite Dirichlet mixture model to improve pattern recognition applications [J] . Nizar Bouguila, Djemel Ziou Pattern recognition letters . 2005,第12期

机译：使用有限Dirichlet混合模型的无监督学习来改善模式识别应用
3. Unsupervised Learning of Gaussian Mixture Model with Application to Image Segmentation [J] . LI Bo, LIU Wenju, DOU Lihua 电子学报：英文版 . 2010,第003期

机译：高斯混合模型的无监督学习及其在图像分割中的应用
4. Unsupervised Learning of a Finite Discrete Mixture Model Based on the Multinomial Dirichlet Distribution: Application to Texture Modeling [C] . Nizar Bouguila, Djemel Ziou International Workshop on Pattern Recognition in Information Systems . 2004

机译：基于多项式Dirichlet分布的有限离散混合模型的无监督学习：纹理建模应用
5. Unsupervised segmentation of noisy and textured images modeled by Markov random fields. [D] . Gregoriou, Georghios Kyriacos. 1992

机译：由Markov随机场建模的噪点和纹理图像的无监督分割。
6. Gaussian mixture model for texture characterization with application to brain DTI images [O] . Luminita Moraru, Simona Moldovanu, Lucian Traian Dimitrievici, 2019

机译：高斯混合模型的纹理表征及其在脑DTI图像中的应用
7. Unsupervised Learning of Finite Mixtures Using Entropy Regularization and Its Application to Image Segmentation [O] . Zhiwu Lu, Yuxin Peng, Jianguo Xiao 2014

机译：基于熵正则化的有限混合无监督学习及其在图像分割中的应用
8. Unsupervised Estimation of Image Textures Using an Autoregressive Model [R] . Bouman, C., Liu, B. 1990

机译：基于自回归模型的无监督估计图像纹理

Unsupervised learning of a finite discrete mixture: Applications to texture modeling and image databases summarization

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