RT Journal Article
T1 Probabilistic Topic Model for Context-Driven Visual Attention Understanding
A1 Fernández Torres, Miguel Ángel
A1 González Díaz, Iván
A1 Díaz de María, Fernando
AB Modern computer vision techniques have to dealwith vast amounts of visual data, which implies a computational effort that has often to be accomplished in broad and challenging scenarios. The interest in efficiently solving these image and video applications has led researchers to develop methods to expertly drive the corresponding processing to conspicuous regions that either depend on the context or are based on specific requirements. In this paper, we propose a general hierarchical probabilistic framework, independent of the application scenario, and relied on the most outstanding psychological studies about attention and eye movements which support that guidance is not based directly on the information provided by early visual processes but on a contextual representation that arose from them. The approach defines the task of context-driven visual attention as a mixture of latent sub-tasks, which are, in turn, modeled as a combination of specific distributions associated to low-, mid-, and high-level spatio-temporal features. Learning from fixations gathered from human observers, we incorporate an intermediate level between feature extraction and visual attention estimation that enables to obtain comprehensively guiding representations. The experiments show how our proposal successfully learns particularly adapted hierarchical explanations of visual attention in diverse video genres, outperforming several leading models in the literature.
PB IEEE
SN 1051-8215
YR 2019
FD 2019-04-04
LK https://hdl.handle.net/10016/30763
UL https://hdl.handle.net/10016/30763
LA eng
NO This work has been partially funded by the Spanish Ministry of Education, Culture and Sports FPU Grant FPU13/04904, and the National Grants TEC2014-53390-P, TEC2014-61729-EXP and TEC2017-84395-P of the Spanish Ministry of Economy and Competitiveness.
DS e-Archivo
RD 30 abr. 2024