Machine learning based video segmentation of moving scene by motion index using IO detector and shot segmentation

作者：

Highlights：

• ESVA-SML is proposed to formulate the learning problem.

• Proposed method is evaluated by SegTrack-v2, DAVIS & Fusion database.

• Spatio-temporal information is employed for semantic video segmentation.

• Fusion produces less RMSE and RAE with minimal response time.

摘要

•ESVA-SML is proposed to formulate the learning problem.•Proposed method is evaluated by SegTrack-v2, DAVIS & Fusion database.•Spatio-temporal information is employed for semantic video segmentation.•Fusion produces less RMSE and RAE with minimal response time.

论文关键词：Video segmentation,Machine learning,Soft voting,Segmentation,Gray level matrix,Audio transformation

论文评审过程：Received 27 November 2021, Revised 7 March 2022, Accepted 24 March 2022, Available online 1 April 2022, Version of Record 11 April 2022.

论文官网地址：https://doi.org/10.1016/j.imavis.2022.104443