基于剪切波域核子空间建模的纹理表示与检索研究

项目名称： 基于剪切波域核子空间建模的纹理表示与检索研究

项目编号： No.61301230

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 无线电电子学、电信技术

项目作者： 董永生

作者单位： 河南科技大学

项目金额： 25万元

中文摘要： 纹理是图像分析和检索中经常提取的关键特征，小波变换是提取图像纹理信息的常用工具。但是，小波变换不能有效地捕获图像纹理中的块状奇异信息，这已成为制约小波变换在图像纹理信息提取应用中的一个主要瓶颈。本项目以具有更强稀疏表示能力的剪切波变换为研究对象，对基于剪切波域核子空间建模的纹理表示和检索进行基础研究。具体研究内容包括：采用核主成分分析，研究剪切波子带系数核子空间的分解规则和建模规律；通过似然函数逼近和正则化方法，研究核主子空间与核次子空间概率模型的联合自适应学习算法；构建基于剪切波域概率模型的纹理表示方法，进而采用马尔科夫链蒙特卡洛技术研究纹理检索问题。本项目旨在建立基于剪切波的图像纹理信息提取方法，为基于纹理内容的图像检索奠定重要的理论基础。同时，本项目也是国内外对核子空间统计建模的一次初探，所提出的一整套新规则和新算法，可望充实高维数据分析的理论与方法体系。

中文关键词： 剪切波；纹理表示；纹理检索；图像分析；图像聚类

英文摘要： Texture is a key feature usually extracted for image analysis and retrieval, and wavelet transforms are common tools that extract the texture information from an image. However, wavelet transforms can not capture piece-wise singularities contained in image texture, which is the main bottleneck constraining the application of wavelet transforms in feature extraction of image texture. This project focuses on shearlets that are sparser than wavelets, and conducts the fundamental research of image texture analysis and retrieval based on kernel subspace modeling in shearlet domains. The main contents include the following three aspects. First, with the help of kernel principal component analysis, decomposition rules and modeling laws in kernel subspaces are studied. Second, a joint and self-adaptive learning algorithm for probability models in the kernel principal subspace and sub-subspace is established by utilizing approximation methods of likelihood function and regularization approaches. Finally, texture of an image is represented by use of probability distributions in shearlet domains, and then texture image retrieval problems are studied by using Monte Carlo techniques. This study aims at building information extraction methods of image texture based on shearlets, which will lay an important theoretical foundat

英文关键词： Shearlets；Texture representation；Texture retrieval；Image analysis；Image clustering