Heterogeneous Image Matching via a Novel Feature Describing Model

Computer vision has been developed greatly in the past several years, and many useful and interesting technologies have been presented and widely applied. Image matching is an important technology based on similarity measurement. In this paper, we propose a novel feature describing model based on scale space and local principle component analysis for heterogeneous image matching. The traditional uniform eight-direction statistics is updated by a task-related k-direction statistics based on prior information of the keypoints. In addition, the k directions are determined by an approximately solution of a Min-Max problem. The principle component analysis is introduced to compute the main directions of local patches based on the gradient field. In addition, the describing vector is formed by then implementing PCA on each sub-patch of a 4 × 4 mesh. Experimental results show the accuracy and efficiency of proposed method.