Indian Journal of Innovations and Developments

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Video Object Co-Segmentation


Affiliations
1 Department of CSE, V.S.B Engineering College, Karur, Tamilnadu, India
 

Objective: The main motivation of this research is to discover and segment out common object regions in different videos.

Methods: The proposed system introduced a spatio-temporal scale-invariant feature transforms (SIFT) flow descriptor which is used to incorporate across-video correspondence. In order to improve the system performance particle swarm optimization (PSO) is used which captures the optimal inter-frame motion based on the position and velocity updation of the particle. In this optimization process, we use a spatio-temporal SIFT flow that integrates optical flow, which captures inter-frame motion, and conventional SIFT flow, which captures across-videos correspondence information. This novel spatio-temporal SIFT flow generates reliable estimations of common foregrounds over the entire video data set.

Findings: The experimental results show that the proposed system achieves better performance compared with existing system in terms of accuracy, precision, recall and f-measure.

Improvement: The proposed algorithm increases the overall system performances by spatio-temporal scale-invariant feature transform flow descriptor and particle swarm optimization algorithm prominently.


Keywords

Salient Object, Object Refinement, Spatio-Temporal SIFT Flow and Particle Swarms.
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  • Video Object Co-Segmentation

Abstract Views: 198  |  PDF Views: 0

Authors

M. Suryaprabha
Department of CSE, V.S.B Engineering College, Karur, Tamilnadu, India
V. Shunmughavel
Department of CSE, V.S.B Engineering College, Karur, Tamilnadu, India

Abstract


Objective: The main motivation of this research is to discover and segment out common object regions in different videos.

Methods: The proposed system introduced a spatio-temporal scale-invariant feature transforms (SIFT) flow descriptor which is used to incorporate across-video correspondence. In order to improve the system performance particle swarm optimization (PSO) is used which captures the optimal inter-frame motion based on the position and velocity updation of the particle. In this optimization process, we use a spatio-temporal SIFT flow that integrates optical flow, which captures inter-frame motion, and conventional SIFT flow, which captures across-videos correspondence information. This novel spatio-temporal SIFT flow generates reliable estimations of common foregrounds over the entire video data set.

Findings: The experimental results show that the proposed system achieves better performance compared with existing system in terms of accuracy, precision, recall and f-measure.

Improvement: The proposed algorithm increases the overall system performances by spatio-temporal scale-invariant feature transform flow descriptor and particle swarm optimization algorithm prominently.


Keywords


Salient Object, Object Refinement, Spatio-Temporal SIFT Flow and Particle Swarms.

References