摘要
图像基学习是图像特征提取与表示的重要方法之一。非负稀疏编码不仅具有标准稀疏编码算法的自适应性、空间的局部性、方向性和频域的带通性,而且更能反应哺乳动物的视觉机制。本文在非负稀疏编码的基础上,利用经验模态分解技术加入了图像的结构信息,提出了结合经验模态分解的非负稀疏编码算法,保证了系数矩阵的稀疏性与所提取图像特征的结构性。学习得到的图像基不仅具有非负稀疏编码的特征,而且更好地表示出图像的结构信息。
Image base learning is one of the important ways of image feature extraction and image expression. Non-negative sparse coding not only features the adaptability of standard sparse coding, spatial localization, orientation, and bandpass in different spatial frequency bands, but also responds to mammal＇s visual mechanism well. On the basis of the non- negative sparse coding, this paper joins the image structure information using the experience modality decomposition technology, proposes a combination of EMD and the non-negative sparse coding algorithm, and ensures the sparseness of the coefficient matrix and the structural characteristics of the image bases extracted. The learned image bases not only have the characteristic of non-negative sparse coding, but express the images structure information well.
出处
《计算机工程与科学》
CSCD
北大核心
2010年第1期77-79,131,共4页
Computer Engineering & Science
基金
陕西省教育厅科研计划资助项目(07JK328)
关键词
图像基
独立分量分析
稀疏编码
经验模态分解
image base
independent component analysis
sparse coding
experience modality decomposition
