2019/10/26 by Liang Shen, Shen, Liang, Jiahua Zhu +7
Computer Science · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Medical Image Segmentation Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.1910.11981
arXiv admin note: text overlap with arXiv:0905.2635 by other authors
arxiv created 2019/10/26 · openalex publication_date 2019/10/26 · arxiv updated 2019/10/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The feature frame is a key idea of feature matching problem between two images. However, most of the traditional matching methods only simply employ the spatial location information (the coordinates), which ignores the shape and orientation information of the local feature. Such additional information can be obtained along with coordinates using general co-variant detectors such as DOG, Hessian, Harris-Affine and MSER. In this paper, we develop a novel method considering all the feature center position coordinates, the local feature shape and orientation information based on Gaussian Mixture Model for co-variant feature matching. We proposed three sub-versions in our method for solving the matching problem in different conditions: rigid, affine and non-rigid, respectively, which all optimized by expectation maximization algorithm. Due to the effective utilization of the additional shape and orientation information, the proposed model can significantly improve the performance in terms of convergence speed and recall. Besides, it is more robust to the outliers.