Efficient Texture Image Retrieval Using Copulas in a Bayesian Framework
Efficient Texture Image Retrieval Using Copulas in a Bayesian Framework
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DOI：
10.1109/tip.2011.2108663
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发表时间：
2011-07
影响因子：
10.6
通讯作者：
R. Kwitt;P. Meerwald;A. Uhl
中科院分区：
文献类型：
--
作者：
R. Kwitt;P. Meerwald;A. Uhl
在本文中，我们研究了一种新颖的双树复小波变换子带系数幅度联合统计模型，然后将其耦合到基于内容的图像检索的贝叶斯框架。联合模型允许捕获相同分解尺度和不同颜色通道的变换系数之间的关联。它进一步有助于纳入最近关于边际系数分布建模的研究工作。我们展示了该新颖模型在四个纹理图像数据库的颜色纹理检索背景下的适用性，并将检索性能与该领域最先进的方法集合进行了比较。我们的实验进一步包括对主要构建块的彻底计算分析、运行时测量以及存储需求分析。最终，我们确定了一种具有低存储要求、有竞争力的检索精度和运行时行为的模型配置，甚至可以在大型图像数据库上进行部署。
In this paper, we investigate a novel joint statistical model for subband coefficient magnitudes of the dual-tree complex wavelet transform, which is then coupled to a Bayesian framework for content-based image retrieval. The joint model allows to capture the association among transform coefficients of the same decomposition scale and different color channels. It further facilitates to incorporate recent research work on modeling marginal coefficient distributions. We demonstrate the applicability of the novel model in the context of color texture retrieval on four texture image databases and compare retrieval performance to a collection of state-of-the-art approaches in the field. Our experiments further include a thorough computational analysis of the main building blocks, runtime measurements, and an analysis of storage requirements. Eventually, we identify a model configuration with low storage requirements, competitive retrieval accuracy, and a runtime behavior, which enables the deployment even on large image databases.