1 research outputs found
Motion-Appearance Interactive Encoding for Object Segmentation in Unconstrained Videos
We present a novel method of integrating motion and appearance cues for
foreground object segmentation in unconstrained videos. Unlike conventional
methods encoding motion and appearance patterns individually, our method puts
particular emphasis on their mutual assistance. Specifically, we propose using
an interactively constrained encoding (ICE) scheme to incorporate motion and
appearance patterns into a graph that leads to a spatiotemporal energy
optimization. The reason of utilizing ICE is that both motion and appearance
cues for the same target share underlying correlative structure, thus can be
exploited in a deeply collaborative manner. We perform ICE not only in the
initialization but also in the refinement stage of a two-layer framework for
object segmentation. This scheme allows our method to consistently capture
structural patterns about object perceptions throughout the whole framework.
Our method can be operated on superpixels instead of raw pixels to reduce the
number of graph nodes by two orders of magnitude. Moreover, we propose to
partially explore the multi-object localization problem with inter-occlusion by
weighted bipartite graph matching. Comprehensive experiments on three benchmark
datasets (i.e., SegTrack, MOViCS, and GaTech) demonstrate the effectiveness of
our approach compared with extensive state-of-the-art methods.Comment: 11 pages, 7 figure