An Effective Keypoint Selection Algorithm in SIFT

Keypoint selection is the important step in object recognition, including general object classification, human tracing and human pose discrimination etc.This paper proposes a more accurate modified key point selection algorithm by modifying SIFT in the stage of extreme point selection. In machine vision or computer vision, including human pose recognition, to select key points, the traditional SIFT completes this according to the extremes derived from LoG (Laplacian of Gaussian) convolution with image, which provides scale invariance features for key points. The extreme points ’ position is the foundation of feature descriptor for the gradient calculation in the next step. But in the process of images convoluting with the difference of Gaussian function to attain the extreme point, bias is produced because the extreme points ’ positions aren’t accurate. We modify the extreme points ’ selection to make key points more accurate with less bias to the theoretical points. Simulation with about 3500 images of different resolutions gives the AIPR (adjusted interest point ratio) and illustrates the universality of extreme points ’ selection and verifies the values of this algorithm