  As constituent parts of image objects, superpixels can improve several
higher-level operations. However, image segmentation methods might have their
accuracy seriously compromised for reduced numbers of superpixels. We have
investigated a solution based on the Iterative Spanning Forest (ISF) framework.
In this work, we present Dynamic ISF (DISF) -- a method based on the following
steps. (a) It starts from an image graph and a seed set with considerably more
pixels than the desired number of superpixels. (b) The seeds compete among
themselves, and each seed conquers its most closely connected pixels, resulting
in an image partition (spanning forest) with connected superpixels. In step
(c), DISF assigns relevance values to seeds based on superpixel analysis and
removes the most irrelevant ones. Steps (b) and (c) are repeated until the
desired number of superpixels is reached. DISF has the chance to reconstruct
relevant edges after each iteration, when compared to region merging
algorithms. As compared to other seed-based superpixel methods, DISF is more
likely to find relevant seeds. It also introduces dynamic arc-weight estimation
in the ISF framework for more effective superpixel delineation, and we
demonstrate all results on three datasets with distinct object properties.
