SIFT Image Features

SIFT - Scale Invariant Feature Transforms

For any object there are many features, interesting points on the object, that can be extracted to provide a "feature" description of the object. This description can then be used when attempting to locate the object in an image containing many other objects. There are many considerations when extracting these features and how to record them. SIFT image features provide a set of features of an object that are not affected by many of the complications experienced in other methods, such as object scaling and rotation.

While allowing for an object to be recognised in a larger image SIFT image features also allow for objects in multiple images of the same location, taken from different positions within the environment, to be recognised. SIFT features are also very resilient to the effects of "noise" in the image.

The SIFT approach, for image feature generation, takes an image and transforms it into a "large collection of local feature vectors" (From "Object Recognition from Local Scale-Invariant Features", David G. Lowe). Each of these feature vectors is invariant to any scaling, rotation or translation of the image. This approach shares many features with neuron responses in primate vision. To aid the extraction of these features the SIFT algorithm applies a 4 stage filtering approach:

  1. Scale-Space Extrema Detection
  2. This stage of the filtering attempts to identify those locations and scales that are identifiable from different views of the same object. This can be efficiently achieved using a "scale space" function. Further it has been shown under reasonable assumptions it must be based on the Gaussian function. The scale space is defined by the function:

    L(x, y, σ) = G(x, y, σ) * I(x, y)

    Where * is the convolution operator, G(x, y, σ) is a variable-scale Gaussian and I(x, y) is the input image.

    Various techniques can then be used to detect stable keypoint locations in the scale-space. Difference of Gaussians is one such technique, locating scale-space extrema, D(x, y, σ) by computing the difference between two images, one with scale k times the other. D(x, y, σ) is then given by:

    D(x, y, σ) = L(x, y, kσ) - L(x, y, σ)

    To detect the local maxima and minima of D(x, y, σ) each point is compared with its 8 neighbours at the same scale, and its 9 neighbours up and down one scale. If this value is the minimum or maximum of all these points then this point is an extrema.

  3. Keypoint Localistaion
  4. This stage attempts to eliminate more points from the list of keypoints by finding those that have low contrast or are poorly localised on an edge. This is achieved by calculating the Laplacian, see for details, value for each keypoint found in stage 1. The location of extremum, z, is given by:

    z =

    If the function value at z is below a threshold value then this point is excluded. This removes extrema with low contrast. To eliminate extrema based on poor localisation it is noted that in these cases there is a large principle curvature across the edge but a small curvature in the perpendicular direction in the defference of Gaussian function. If this difference is below the ratio of largest to smallest eigenvector, from the 2x2 Hessian matrix at the location and scale of the keypoint, the keypoint is rejected.

  5. Orientation Assignment
  6. This step aims to assign a consistent orientation to the keypoints based on local image properties. The keypoint descriptor, described below, can then be represented relative to this orientation, achieving invariance to rotation. The approach taken to find an orientation is:

  7. Keypoint Descriptor
  8. The local gradient data, used above, is also used to create keypoint descriptors. The gradient information is rotated to line up with the orientation of the keypoint and then weighted by a Gaussian with variance of 1.5 * keypoint scale. This data is then used to create a set of histograms over a window centred on the keypoint.

    Keypoint descriptors typically uses a set of 16 histograms, aligned in a 4x4 grid, each with 8 orientation bins, one for each of the main compass directions and one for each of the mid-points of these directions. This results in a feature vector containing 128 elements.

These resulting vectors are know as SIFT keys and are used in a nearest-neigbours approach to identify possible objects in an image. Collections of keys that agree on a possible model are identfied, when 3 or more keys agree on the model parameters this model is evident in the image with high probability. Due to the large number of SIFT keys in an image of an object, typically a 500x500 pixel image will generate in the region of 2000 features, substantial levels of occlusion are possible while the image is still recognised by this technique, see Object Recognition from Local Scale-Invariant Features for examples of this.