A Fast Alpha-tree Algorithm for Extreme Dynamic Range Pixel Dissimilarities

The α-tree algorithm is a useful hierarchical representation technique which facilitates comprehension of images
such as remote sensing and medical images. Most α-tree algorithms make use of priority queues to process image edges
in a correct order, but because traditional priority queues are
inefficient in α-tree algorithms using extreme-dynamic-range
pixel dissimilarities, they run slower compared with other related
algorithms such as component tree. In this paper, we propose
a novel hierarchical heap priority queue algorithm that can
process α-tree edges much more efficiently than other stateof-the-art priority queues. Experimental results using 48-bit
Sentinel-2A remotely sensed images and randomly generated
images have shown that the proposed hierarchical heap priority
queue improved the timings of the flooding α-tree algorithm by
replacing the heap priority queue with the proposed queue: 1.68
times in 4-N and 2.41 times in 8-N on Sentinel-2A images, and
2.56 times and 4.43 times on randomly generated images.