Paper Info
Title
FOMTrace: Interactive Video Segmentation By Image Graphs and Fuzzy Object Models.
Abstract
Common users have changed from mere consumers to active producers of multimedia data content. Video editing plays an important role in this scenario, calling for simple segmentation tools that can handle fast-moving and deformable video objects with possible occlusions, color similarities with the background, among other challenges. We present an interactive video segmentation method, named FOMTrace, which addresses the problem in an effective and efficient way. From a user-provided object mask in a first frame, the method performs semi-automatic video segmentation on a spatiotemporal superpixel-graph, and then estimates a Fuzzy Object Model (FOM), which refines segmentation of the second frame by constraining delineation on a pixel-graph within a region where the objectu0027s boundary is expected to be. The user can correct/accept the refined object mask in the second frame, which is then similarly used to improve the spatiotemporal video segmentation of the remaining frames. Both steps are repeated alternately, within interactive response times, until the segmentation refinement of the final frame is accepted by the user. Extensive experiments demonstrate FOMTraceu0027s ability for tracing objects in comparison with state-of-the-art approaches for interactive video segmentation, supervised, and unsupervised object tracking.
Year
Venue
Field
2016
arXiv: Computer Vision and Pattern Recognition
Interactive video,Computer vision,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Object model,Segmentation-based object categorization,Image segmentation,Video tracking,Video editing,Artificial intelligence
DocType
Volume
Citations 
Journal
abs/1606.03369
2
PageRank 
References 
Authors
0.38
0
2
Name
Order
Citations
PageRank
Thiago Vallin Spina1457.32
Alexandre Xavier Falcão216710.22