摘要
针对视觉词袋(Bag-of-visual-words,BOVW)模型直方图量化误差大的缺点,提出基于稀疏编码的图像检索算法.由于大多数图像特征属于非线性流形结构,传统稀疏编码使用向量空间对其度量必然导致不准确的稀疏表示.考虑到图像特征空间的流形结构,选择对称正定矩阵作为特征描述子,构建黎曼流形空间.利用核技术将黎曼流形结构映射到再生核希尔伯特空间,非线性流形转换为线性稀疏编码,获得图像更准确的稀疏表示.实验在Corel1000和Caltech101两个数据集上进行,与已有的图像检索算法对比,提出的图像检索算法不仅提高了检索准确率,而且获得了更好的检索性能.
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, trot 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 umnifold 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.
出处
《自动化学报》
EI
CSCD
北大核心
2017年第5期778-788,共11页
Acta Automatica Sinica
基金
国家自然科学基金(61201323)资助~~
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
Sparse coding, Riemannian geometry, manifold structure, symmetric positive definite matrix, Hilbert space, image retrieval
