Title: Flow-free Video Object Segmentation,
Abstract: Segmenting foreground object from a video is a challenging task because of
the large deformations of the objects, occlusions, and background clutter. In
this paper, we propose a frame-by-frame but computationally efficient approach
for video object segmentation by clustering visually similar generic object
segments throughout the video. Our algorithm segments various object instances
appearing in the video and then perform clustering in order to group visually
similar segments into one cluster. Since the object that needs to be segmented
appears in most part of the video, we can retrieve the foreground segments from
the cluster having maximum number of segments, thus filtering out noisy
segments that do not represent any object. We then apply a track and fill
approach in order to localize the objects in the frames where the object
segmentation framework fails to segment any object. Our algorithm performs
comparably to the recent automatic methods for video object segmentation when
benchmarked on DAVIS dataset while being computationally much faster.