English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recent works on interactive video object cutout mainly focus on designingdynamic foreground-background (FB) classifiers for segmentation propagation.However, the research on optimally removing errors from the FB classificationis sparse, and the errors often accumulate rapidly, causing significant errorsin the propagated frames. In this work, we take the initial steps to addressingthis problem, and we call this new task \emph{segmentation rectification}. Ourkey observation is that the possibly asymmetrically distributed false positiveand false negative errors were handled equally in the conventional methods. We,alternatively, propose to optimally remove these two types of errors. To thiseffect, we propose a novel bilayer Markov Random Field (MRF) model for this newtask. We also adopt the well-established structured learning framework to learnthe optimal model from data. Additionally, we propose a novel one-classstructured SVM (OSSVM) which greatly speeds up the structured learning process.Our method naturally extends to RGB-D videos as well. Comprehensive experimentson both RGB and RGB-D data demonstrate that our simple and effective methodsignificantly outperforms the segmentation propagation methods adopted in thestate-of-the-art video cutout systems, and the results also suggest thepotential usefulness of our method in image cutout system.

Segmentation Rectification for Video Cutout via One-Class Structured Learning