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Dense SIFT collects more features at each location and scale in an image, increasing recognition accuracy accordingly. However, computational complexity will always be an issue for it (in relation to normal SIFT).
Bin size vs keypoint scale. DSIFT specifies the descriptor size by a single parameter, size , which controls the size of a SIFT spatial bin in pixels. In the standard SIFT descriptor, the bin size is related to the SIFT keypoint scale by a multiplier, denoted magnif below, which defaults to 3 .
Histograms of oriented gradients (HOG) computed over a grid in the image domain. In contrast to SIFT descriptor, which is a local image descriptor, the resulting histograms of oriented gradients (HOG) descriptor is a regional image descriptor.
This is due to the SIFT-like descriptor being more abstract than a normalized patch in regards to the actual pixel values of the image. Therefore, it is more difficult to match up keypoints than when using normalized patches, since there is less data about the actual image.
What is the difference between the dense sift implementation compare to sift? What are the advantages/disadvantages of one to another? I'm talking in particular about the VLFeat implementations.
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