Unsupervised image-set clustering using an information theoretic framework
Unsupervised image-set clustering using an information theoretic framework
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DOI：
10.1109/tip.2005.860593
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发表时间：
2006-02-01
影响因子：
10.6
通讯作者：
Greenspan, H
中科院分区：
文献类型：
--
作者：
Goldberger, J;Gordon, S;Greenspan, H
在本文中，我们将离散和连续图像模型与基于信息理论的无监督分层图像集聚类标准相结合。连续图像建模是基于高斯密度的混合模型。无监督图像集聚类是基于最近引入的信息理论原理的广义版本，即信息瓶颈原理。对图像进行聚类，使得最大限度地保留聚类和图像内容之间的相互信息。实验结果证明了该框架在大图像集上的聚类性能。利用信息理论工具对聚类质量进行评价。特别强调了聚类在高效图像搜索和检索中的应用。
In this paper, we combine discrete and continuous image models with information-theoretic-based criteria for unsupervised hierarchical image-set clustering. The continuous image modeling is based on mixture of Gaussian densities. The unsupervised image-set clustering is based on a generalized version of a recently introduced information-theoretic principle, the information bottleneck principle. Images are clustered such that the mutual information between the clusters and the image content is maximally preserved. Experimental results demonstrate the performance of the proposed framework for image clustering on a large image set. Information theoretic tools are used to evaluate cluster quality. Particular emphasis is placed on the application of the clustering for efficient image search and retrieval.