Pattern mining-based video saliency detection: Application to moving object segmentation. (August 2018)

• Record Type: Journal Article
• Authors: Ramadan, Hiba; Tairi, Hamid
• Is Part Of: Computers & electrical engineering. Volume 70(2018)
• Page Start: 567
• Page End: 579
• Publication Date: 2018-08
• Subjects: Spatiotemporal saliency -- Pattern mining algorithm -- Spatiotemporal saliency patterns -- Image saliency -- Motion saliency -- Moving object segmentation -- Energy minimization; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.08.029
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In this paper, we present a new model for spatiotemporal saliency detection. Instead of previous works which combine the image saliency in the spatial domain with motion cues to build their video saliency model, we propose to apply the pattern mining (PM) algorithm. From initial saliency maps computed in spatial and temporal domains, discriminative spatiotemporal saliency patterns can be recognized and their label information is propagated to obtain the final saliency map. Our model ensures a good compromise between image saliency and motion saliency and presents an accurate prediction to estimate salient regions in comparison with other methods for video saliency detection. Finally, as an application of our algorithm, our spatiotemporal saliency map is combined with appearance models and dynamic location models into an energy minimization framework to segment salient moving object. Experiments show a good performance of our algorithm for moving object segmentation (MOS) on benchmark datasets.