Paper Info
Title
Dynamic saliency models and human attention: a comparative study on videos
Abstract
Significant progress has been made in terms of computational models of bottom-up visual attention (saliency). However, efficient ways of comparing these models for still images remain an open research question. The problem is even more challenging when dealing with videos and dynamic saliency. The paper proposes a framework for dynamic-saliency model evaluation, based on a new database of diverse videos for which eye-tracking data has been collected. In addition, we present evaluation results obtained for 4 state-of-the-art dynamic-saliency models, two of which have not been verified on eye-tracking data before.
Year
DOI
Venue
2012
10.1007/978-3-642-37431-9_45
ACCV (3)
Keywords
Field
DocType
dynamic-saliency model evaluation,new database,eye-tracking data,efficient way,human attention,state-of-the-art dynamic-saliency model,computational model,comparative study,dynamic saliency,present evaluation result,bottom-up visual attention,diverse video,dynamic saliency model
Open research,Computer vision,Salience (neuroscience),Computer science,Visual attention,Computational model,Independent component analysis,Artificial intelligence,Visual saliency
Conference
Citations 
PageRank 
References 
21
0.86
15
Authors
6
Name
Order
Citations
PageRank
Nicolas Riche11849.75
Matei Mancas231527.50
Dubravko Culibrk327920.02
Crnojević, V.4452.45
Bernard Gosselin519812.88
Thierry Dutoit61006123.84