关键词: 轮廓相似度, 稀疏表示, 拓扑相似性, 图像匹配

Abstract:

In order to solve the problems of low matching efficiency, high time complexity and high computational complexity in image matching algorithms, an image matching algorithm is proposed by combining sparse representation and topological similarity. The algorithm first performs feature detection on the image, calculates the contour similarity, finds the largest contour area similar in the image to be matched, uses sparse coding to sparsely represent the features in the contour, establishes a sparse model, and simplifies complex features, but without affecting the classification method of features, the features of the same category or the same attribute are classified into the same feature set, and the sparse representation and the mutual attribute learning of the neighboring mutual information are combined to learn. The transformation matrix is calculated to represent the image, and the structured topological similarity is used to optimize the points associated with the inside and outside of the contour. Finally, the algorithm is analyzed from subjective evaluation and objective evaluation. The results show that compared with other image matching algorithms, the proposed new algorithm has obvious matching accuracy and effect. It is concluded that the new algorithm has better advantages in improving matching efficiency and complexity.

Key words: contour similarity, sparse representation, topological similarity, image matching