SIFT 特征及其代码

SIFT原文：Distinctive Image Features from Scale-Invariant Keypoints，作者David G. Lowe 发表在2004的IJCV上面，这篇文章可谓是里程碑似的工作，现在的他引次数已经达到12239次！！

作者在这方面至少有10+年的积累才发出这种牛B的文章，所以这也暗示我们要沿着一个方向踏踏实实的做呀~

现在关于图像分类的文章底层特征基本上都是SIFT，所以这种经典的文章还是要了解一下呀，虽然那个IJCV很长，在有些细节说的还是不具体，所以可以看看Rob Hess写的代码，点击即可下载，代码写的很工整，非常容易理解，用C写的。

SIFT Library

The Scale Invariant Feature Transform (SIFT) is a method to detect distinctive, invariant image feature points, which easily can be matched between images to perform tasks such as object detection and recognition, or to compute geometrical transformations between images. The open-source SIFT library available here is implemented in C using the OpenCV open-source computer vision library and includes functions for computing SIFT features in images, matching SIFT features between images using kd-trees, and computing geometrical image transforms from feature matches using RANSAC.  The library also includes functionality to import and work with image features from both David Lowe’sSIFT detector and the Oxford VGG’s affine covariant feature detectors. The images below depict some of this functionality.

SIFT features detected in two images

SIFT features matched between the two images and the transform computed from the matches using RANSAC.

不过做实验的时候还是推荐使用牛津大学开发的VLFeat，速度很快，而且性能很好。

The VLFeat open source library implements popular computer vision algorithms including SIFT,MSER, k-means, hierarchical k-means, agglomerative information bottleneck, and quick shift. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. The latest version of VLFeat is 0.9.13.