An improved Scale Invariant Feature Transform algorithm based on weighted principal component analysis for image matching

Since first proposed, SIFT (Scale Invariant Feature Transform) has attracted great attention in the field of computer vision because of many of its advantages. In the paper, we propose a novel SIFT algorithm based on Weighted-PCA. Besides, in order to improve the matching accuracy, we redefine the distance measurement in the matching process. The experimental results show that the proposed method is more effective than existing ones under image rotation, scale transformation and noise degradation.