Documentation>MATLAB API>SIFT - vl_dsift

[FRAMES,DESCRS] = VL_DSIFT(I) extracts a dense set of SIFT features from image I. I must be of class SINGLE and grayscale. FRAMES is a 2 x NUMKEYPOINTS, each colum storing the center (X,Y) of a keypoint frame (all frames have the same scale and orientation). DESCRS is a 128 x NUMKEYPOINTS matrix with one descriptor per column, in the same format of VL_SIFT().

VL_DSIFT() does NOT compute a Gaussian scale space of the image I. Instead, the image should be pre-smoothed at the desired scale level, e.b. by using the VL_IMSMOOTH() function.

The scale of the extracted descriptors is controlled by the option SIZE, i.e. the width in pixels of a spatial bin (recall that a SIFT descriptor is a spatial histogram with 4 x 4 bins).

The sampling density is controlled by the option STEP, which is the horizontal and vertical displacement of each feature cetner to the next.

The sampled image area is controlled by the option BOUNDS, defining a rectangle in which features are comptued. A descriptor is included in the rectangle if all the centers of the spatial bins are included. The upper-left descriptor is placed so that the uppler-left spatial bin center is algined with the upper-left corner of the rectangle.

By default, VL_DSIFT() computes features equivalent to VL_SIFT(). However, the FAST option can be used to turn on an variant of the descriptor (see VLFeat C API documentation for further details) which, while not strictly equivalent, it is much faster.

VL_DSIFT() accepts the following options:

Step 1

Extracts a SIFT descriptor each STEP pixels.

Size 3

A spatial bin covers SIZE pixels.

Bounds [whole image]

Specifies a rectangular area where descriptors should be extracted. The format is [XMIN, YMIN, XMAX, YMAX]. If this option is not specified, the entiere image is used. The bounding box is clipped to the image boundaries.


If specified, adds to the FRAMES ouptut argument a third row containint the descriptor norm, or engergy, before contrast normalization. This information can be used to suppress low contrast descriptors.


If specified, use a piecewise-flat, rather than Gaussian, windowing function. While this breaks exact SIFT equivalence, in practice is much faster to compute.


If specified, the descriptor are returned in floating point rather than integer format.

Geomerty [4 4 8]

Specify the geometry of the descriptor as [NX NY NO], where NX is the number of bin in the X direction, NY in the Y direction, and NO the nubmer of orientation bins.


If specified, be verbose.


In the standard SIFT detector/descriptor, implemented by VL_SIFT(), the size of a spatial bin is related to the keypoint scale by a multiplier, called magnification factor, and denoted MAGNIF. Therefore, the keypoint scale corresponding to the descriptors extracted by VL_DSIFT() is equal to SIZE / MAGNIF. VL_DSIFT() does not use MAGNIF because, by using dense sampling, it avoids detecting keypoints in the first plance.

VL_DSIFT() does not smooth the image as SIFT does. Therefore, in order to obtain equivalent results, the image should be pre-smoothed approriately. Recall that in SIFT, for a keypoint of scale S, the image is pre-smoothed by a Gaussian of variance S.^2 - 1/4 (see VL_SIFT() and VLFeat C API documentation).


This example produces equivalent SIFT descriptors using VL_DSIFT() and VL_SIFT():

 binSize = 8 ;
 magnif = 3 ;
 Is = vl_imsmooth(I, sqrt((binSize/magnif)^2 - .25)) ;

 [f, d] = vl_dsift(Is, 'size', binSize) ;
 f(3,:) = binSize/magnif ;
 f(4,:) = 0 ;
 [f_, d_] = vl_sift(I, 'frames', f) ;

The equivalence is never exact due to (i) boundary effects and (ii) the fact that VL_SIFT() downsamples the image to save computation. It is, however, usually very good.


In categorization it is often useful to under-smooth the image, comared to standard SIFT, in order to keep the gradients sharp.


As mentioned, the VL_DSIFT() descriptors cover the bounding box specified by BOUNDS = [XMIN YMIN XMAX YMAX]. Thus the top-left bin of the top-left descriptor is placed at (XMIN, YMIN). The next three bins to the right are at XMIN + SIZE, XMIN + 2*SIZE, XMIN + 3*SIZE. The X coordiante of the center of the first descriptor is therefore at (XMIN + XMIN + 3*SIZE) / 2 = XMIN + 3/2 * SIZE. For instance, if XMIN = 1 and SIZE = 3 (default values), the X coordinate of the center of the first descriptor is at 1 + 3/2 * 3 = 5.5. For the second descriptor immediately to its right this is 5.5 + STEP, and so on.

See also: VL_SIFT(), VL_HELP().