Paper Info
Title
Pattern mining-based video saliency detection: Application to moving object segmentation.
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.
Year
DOI
Venue
2018
10.1016/j.compeleceng.2017.08.029
Computers & Electrical Engineering
Keywords
Field
DocType
Spatiotemporal saliency,Pattern mining algorithm,Spatiotemporal saliency patterns,Image saliency,Motion saliency,Moving object segmentation,Energy minimization
Motion cues,Computer vision,Saliency map,Pattern recognition,Kadir–Brady saliency detector,Salience (neuroscience),Computer science,Segmentation,Artificial intelligence,Discriminative model,Salient,Energy minimization
Journal
Volume
ISSN
Citations 
70
0045-7906
0
PageRank 
References 
Authors
0.34
20
2
Name
Order
Citations
PageRank
Hiba Ramadan132.42
Hamid Tairi25717.49