The EHD basically represents the distribution of 5 types of
edges in each local area called a sub-image. As shown in Fig. 1,
the sub-image is defined by dividing the image space into 4×4
nonoverlapping blocks. Thus, the image partition always yields
16 equal-sized sub-images regardless of the size of the original
image. To characterize the sub-image, we then generate a his-
togram of edge distribution for each sub-image. Edges in the
sub-images are categorized into 5 types: vertical, horizontal,
45-degree diagonal, 135-degree diagonal, and non-directional
edges (Fig. 2).
Thus, the histogram for each sub-image repre-
sents the relative frequency of occurrence of the 5 types of
edges in the corresponding sub-image. As a result, as shown in
each local histogram contains 5 bins. Each bin corre-
sponds to one of 5 edge types. Since there are 16 sub-images in
the image, a total of 5×16=80 histogram bins is required, (Fig.
Note that each of the 80-histogram bins has its own seman-
tics in terms of location and edge type. For example, the bin for
the horizontal type edge in the sub-image located at (0,0) in Fig.
1 carries the information of the relative population of the hori-
zontal edges in the top-left local region of the image.