.png){kind=icon}
.png){kind=icon}
Return
Lightweight Probabilistic Texture Retrieval
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2009.2032313.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Kullback-Leibler divergence
texture image retrieval
wavelets
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W