一种低冗余Dense SIFT特征提取方法

摘要

特征提取是图像分类的关键部分之一.现有的Dense SIFT特征采用固定网格和步长以从上到下、从左到右的重叠方式提取特征,如果图像分辨率过大,将会导致提取的图像特征数量非常大,并且引入大量的冗余信息.为此,提出了一种低冗余Dense SIFT特征提取方法.该方法首先对图像进行预处理,实现对图像的紧凑表示;然后,利用数据中心化思想和(l)0范数去除冗余的Dense SIFT特征点,节约特征存储所需的空间,降低后续处理的计算复杂度;最后,将低冗余Dense SIFT特征提取方法应用于图像分类,提出了一种图像分类方案.实验结果表明,采用所提出的Dense SIFT特征提取方法,在减少特征点数量的同时,可以提升特征的区分能力.%Feature extraction is one of the key parts in image classification.The existing Dense SIFT feature method adopts fixed grid and step-size to extract features by scanning way from top to bottom and left to right.If image resolution is too high,more image features will be extracted,so that a lot of redundancy information will be introduced.Therefore,a low-redundancy Dense SIFT feature extraction algorithm is proposed.In this al gorithm,the preprocessing is executed on the image,which can produce the compact expression of image.Then,the centralization idea and the e0 norm are exploited to optimize Dense SIFT features for removing the re dundant feature points,in order to finally improve the description ability of features.Finally,the low-redundancy Dense SIFT is applied to image classification.Experimental results show that the proposed scheme can reduce the number of feature descriptors and improve the performance of feature.