Publication: Goal-oriented top-down probabilistic visual attention model for recognition of manipulated objects in egocentric videos
Loading...
Identifiers
Publication date
2015-11
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Impact
Export
Abstract
We propose a new top down probabilistic saliency model for egocentric video content. It aims to predict top-down visual attention maps focused on manipulated objects, that are then used for psycho-visual weighting of features in the problem of manipulated object recognition. The model is probabilistically defined using both global and local appearance features extracted from automatically segmented arm areas and objects. A psycho-visual experiment has been conducted in a guided framework that compares our proposal and other popular state-of-the-art models with respect to human gaze fixations. The obtained results show that our approach outperforms several popular bottom-up saliency approaches in a well-known egocentric dataset Furthermore, an additional task-driven assessment for object recognition in egocentric video reveals that the proposed method improves the performance of several state-of-the-art techniques for object detection.
Description
Keywords
Saliency maps, Egocentric vision, Object recognition, Vision modelling, Image processing, Video processing
Bibliographic citation
Buso, V., González-Díaz, I. & Benois-Pineau, J. (2015). Goal-oriented top-down probabilistic visual attention model for recognition of manipulated objects in egocentric videos. Signal Processing: Image Communication, vol. 39, part B, pp. 418–431.