Temporally-Dependent Dirichlet Process Mixtures for Egocentric Video Segmentation

2014 ◽  
Author(s):  
Joseph W. Barker ◽  
James W. Davis
Keyword(s):  
Dirichlet Process ◽  
Video Segmentation ◽  
Dependent Dirichlet Process ◽  
Dirichlet Process Mixtures

scholarly journals Video object extraction using optimized smoothed dirichlet process multi-view learning with improved adaptive modified Markova random field

2018 ◽  
Vol 7 (4) ◽  
pp. 2598
Author(s):  
G S. Gowri ◽  
Dr. P. Ponmuthuramalingam
Keyword(s):  
Random Field ◽  
Dirichlet Process ◽  
Video Segmentation ◽  
Target Object ◽  
Object Extraction ◽  
Contour Tracking ◽  
Video Object ◽  
Object Boundary ◽  
Current Frame ◽  
Film Making

Video object extraction (VOE) using segmentation from a video sequence is a very important task in editing and multimedia analysis for film making. Most of the VOE approaches required prior knowledge about background and foreground to extract target objects. In this paper, an Optimized smoothed Dirichlet Process Multi-view learning with improved adaptive Modified Markov Random Field which is enhanced by adaptive shape prior modified graph cut (OsDPMVL-IASMMRF) model has been extended for video-based object extraction. The contour tracking has been additionally included OsDPMVL-IASMMRF for VOE. The Teh–Chin algorithm has been used with OsDPMVL-IASMMRF for predicting the contour in the current frame by matching the extracted object contour from the previous segmented frame. The contour tracking propagates the shape of the target object, whereas the OsDPMVL-IASMMRF segmentation refined the object boundary and the shape for enhancing the accuracy of video segmentation. The experimental outcomes show that the proposed approach provides better segmentation results in terms of accuracy, precision and recall.  

Sign in / Sign up

Export Citation Format

Share Document