  Finding correspondences between structural entities decomposing images is of
high interest for computer vision applications. In particular, we analyze how
to accurately track superpixels - visual primitives generated by aggregating
adjacent pixels sharing similar characteristics - over extended time periods
relying on unsupervised learning and temporal integration. A two-step video
processing pipeline dedicated to long-term superpixel tracking is proposed.
First, unsupervised learning-based superpixel matching provides correspondences
between consecutive and distant frames using new context-rich features extended
from greyscale to multi-channel and forward-backward consistency contraints.
Resulting elementary matches are then combined along multi-step paths running
through the whole sequence with various inter-frame distances. This produces a
large set of candidate long-term superpixel pairings upon which majority voting
is performed. Video object tracking experiments demonstrate the accuracy of our
elementary estimator against state-of-the-art methods and proves the ability of
multi-step integration to provide accurate long-term superpixel matches
compared to usual direct and sequential integration.
