摘要:
提出了一种新的基于局部不变映射(Locality preserving projections, LPP)的描述器设计算法. 该算法用LPP预先生成一个特征矩阵, 接着把特征点邻域内所有点的梯度组成一个高维的梯度向量, 然后通过特征矩阵把该梯度向量嵌入到一个低维的流形空间中, 生成一个维数很低的向量, 并把它作为该特征点的描述器. 所提出的算法能保持描述器之间的几何结构不变: 原空间中邻接的描述器映射到低维空间后保持邻接, 而不相似的描述器映射后区分度更大, 所以该算法所生成的描述器能表现特征点之间的内在关系, 具有很强的鲁棒性. 通过与SIFT (Scale invariant feature transform), PCA-SIFT的实验比较, 此算法更快速, 更具鲁棒性.
Abstract:
This paper presents a novel algorithm to design the descriptor of image feature points based on locality preserving projections (LPP). Firstly, an eigenmatrix was pre-produced by LPP, and a high-dimensional gradient vector was constructed by the gradient vectors of all neighborhood points around feature points. Then, the high-dimensional gradient vector was embedded into a lower dimensional manifold space with the eigenmatrix, and a low-dimensional descriptor of the feature points was generated. The proposed algorithm can preserve invariability on the geometric structure: the eigenvectors which are neighboring each other in the original space will maintain the same attribute in low-dimensional space; on the contrary, the unsimilar eigenvectors become apart farther each other. Therefore, the description generated by our algorithm can show the interrelationship between features and has strong robustness. Moreover, the comparative experiments illustrated that the proposed algorithm is more rapid and accurate than SIFT and PCA-SIFT.