自动化学报2017，Vol.43Issue(5)：778-788,11.DOI:10.16383/j.aas.2017.c150838
基于黎曼流形稀疏编码的图像检索算法
An Image Retrieval Method with Sparse Coding Based on Riemannian Manifold
摘要
Abstract
In the BOVW (bag-of-visual-words) model, histogram quantization would result in a bigger error for image retrieval. Considering this shortcoming, a new image retrieval algorithm based on sparse coding is proposed. Most image features belongs to nonlinear manifold structure, but the traditional sparse coding uses vector space to measure image feature space, which must lead to an inaccurate sparse representation. Owing to the manifold structure of image features space, symmetric positive definite matrices are selected as feature descriptors to build a Riemannian manifold space. Through the kernel method, the Riemann manifold structure is mapped into the reproducing kernel Hilbert space, and nonlinear manifold is converted into linear sparse coding, so the image can acquire a more accurate sparse representation. Experiments are performed on the Corel1000 database and Caltech101 database. In comparison with the existing image retrieval algorithms, the new image retrieval algorithm largely improves the retrieval accuracy and has a better efficiency.关键词
稀疏编码/黎曼几何/流形结构/对称正定矩阵/希尔伯特空间/图像检索Key words
Sparse coding/Riemannian geometry/manifold structure/symmetric positive definite matrix/Hilbert space/image retrieval引用本文复制引用
王瑞霞,彭国华..基于黎曼流形稀疏编码的图像检索算法[J].自动化学报,2017,43(5):778-788,11.基金项目
国家自然科学基金(61201323) 资助 Supported by National Natural Science Foundation of China(61201323) （61201323）
