Lightweight Probabilistic Texture Retrieval
Lightweight Probabilistic Texture Retrieval
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DOI：
10.1109/tip.2009.2032313
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发表时间：
2010
影响因子：
10.6
通讯作者：
R. Kwitt;A. Uhl
中科院分区：
文献类型：
--
作者：
R. Kwitt;A. Uhl
本文从计算的角度考虑了小波域概率图像检索的框架。我们不仅关注实现高检索率，而且讨论了可能妨碍实际应用的性能瓶颈。基于对小波变换系数边缘分布建模的研究，提出了一种新的检索方法。我们工作的基石是双树复小波变换和系数大小的一些统计模型。图像相似性测量是通过使用统计模型之间的Kullback-Leibler散度的封闭形式解决方案来完成的。我们对相似性测量和模型参数估计所需的算术运算的数量进行了深入的计算分析。在一个广泛使用的纹理图像数据库上的检索实验结果表明，我们以较低的计算成本获得了具有竞争力的检索结果。
This paper contemplates the framework of probabilistic image retrieval in the wavelet domain from a computational point of view. We not only focus on achieving high retrieval rates, but also discuss possible performance bottlenecks which might prevent practical application. We propose a novel retrieval approach which is motivated by previous research work on modeling the marginal distributions of wavelet transform coefficients. The building blocks of our work are the dual-tree complex wavelet transform and a number of statistical models for the coefficient magnitudes. Image similarity measurement is accomplished by using closed-form solutions for the Kullback-Leibler divergences between the statistical models. We provide an in-depth computational analysis regarding the number of arithmetic operations required for similarity measurement and model parameter estimation. The experimental retrieval results on a widely used texture image database show that we achieve competitive retrieval results at low computational cost.