LAUSR

ABSTRACT Image matching is a quite time-consuming task for Structure-from-Motion (SfM). In this paper, a Bag-of-Words (BoW) model that reduces the feature dimensions and introduces image spatial locations is proposed to improve the efficiency and reliability of SfM. The whole workflow includes three steps. Firstly, principal component analysis (PCA) is used to reduce the high-dimensional features to low-dimensional features, so as to improve the efficiency of retrieval vocabulary construction. Secondly, by calculating the inverse distance weighting score of query images, a comprehensive retrieval score is constructed to improve the distinguishability between similar images. Finally, by calculating the retrieval threshold and discarding the invalid matching image pairs, the image query precision is further improved. The experimental results show that compared with the VocabTree (VT) and the Hamming Embedding (HE) methods, the proposed algorithm for image matching time is reduced by 69.5% and 72.0%, respectively, while the number of sparse point clouds of the reconstruction is increased 0.6%.