I am reading the paper Selective Search for Object Recognition (here)[http://www.huppelen.nl/publications/selectiveSearchDraft.pdf]. In Section 3.2, they give a similarity measure between two regions of an image based on the texture of the regions with what they refer to as "fast SIFT-like measurements". On page 4, bottom right side of the page, they write:

We take Gaussian derivatives in eight orientations using

$\sigma = 1$ for each colour channel. For each orientation for each colour channel we extract a histogram using a bin size of $10$.

I understand that a derivative of Gaussian filter is the filter of size $n \times n$ consisting of a discrete approximation of the derivative of a bivariate gaussian function of mean $0$ with some standard deviation.

What do the authors mean by "with eight orientations"? Is this some kind of modification to the filter? Any insights appreciated.

I am reading the paper Selective Search for Object Recognition here. In Section 3.2, they give a similarity measure between two regions of an image based on the texture of the regions with what they refer to as "fast SIFT-like measurements". On page 4, bottom right side of the page, they write:

We take Gaussian derivatives in eight orientations using

$\sigma = 1$ for each colour channel. For each orientation for each colour channel we extract a histogram using a bin size of $10$.

I understand that a derivative of Gaussian filter is the filter of size $n \times n$ consisting of a discrete approximation of the derivative of a bivariate gaussian function of mean $0$ with some standard deviation.

What do the authors mean by "with eight orientations"? Is this some kind of modification to the filter? Any insights appreciated.