Optimization algorithm for extracting independent sift key points based on principal component analysis

Abstract

The invention discloses an optimization algorithm for extracting independent sift key points based on principal component analysis. The optimization algorithm comprises the processing steps of firstlyperforming histogram matching on histograms of sift feature points obtained by a sift algorithm and surrounding pixels, and enabling the histograms to be in Gaussian distribution; secondly, when thehistograms are enabled to serve as inputs of a pca algorithm, indicating that principal components output by pca are mutually independent; and finally, mapping the principal components to histograms of original sift feature points and surrounding pixels through eigenvalues, and finishing the screening of the sift feature points, wherein the obtained sift feature points are mutually independent. The optimization algorithm has the beneficial effects that for the problem of a matching error caused by similarity of the sift feature points, the method for extracting the independent sift key pointsby utilizing the pca is proposed; the screening of the sift feature points is finished; and the to-be-matched key points are mutually independent, so that the probability of the matching error is reduced.

Description

technical field [0001] The invention relates to an optimization algorithm for extracting independent SIFT key points based on principal component analysis, and belongs to the technical field of image processing. Background technique [0002] In image processing, image features are of great significance. At present, commonly used image features include geometric features, color features, texture features, and feature points, and these features have been widely used in object recognition, motion estimation and other fields. Extracting robust image features is very important in practical applications. Scale-invariant feature points have strong adaptability to image transformations such as illumination, scale, rotation, and scaling. Feature points are important features of images. Compared with other image features, scale-invariant feature points have the advantages of being invariant to rotation and not changing with illumination. Image processing using scale-invariant featu...

AI Technical Summary

Problems solved by technology

However, the sift algorithm is computationally complex, and since the algorithm extracts 128-dimensional descriptors, the speed of image matching and retrieval is reduced

To address this shortcoming, someone proposed a fast matching algorithm based on the combination of SIFT algorithm and Principal Component Analysis (pca), that is, the SIFT-PCA algorithm. The main goal of this algorithm is to use the PCA algorithm to reduce the dimension of the SIFT descriptor. In this way, the fast matching between descriptors can be achieved, but the problem of matching errors caused by the similarity of sift feature points cannot be avoided

Images