Publication: Perceptual modeling in the problem of active object recognition in visual scenes
Loading...
Identifiers
Publication date
2016-08
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Impact
Export
Abstract
Incorporating models of human perception into the process of scene interpretation and object recognition in visual content is a strong trend in computer vision. In this paper we tackle the modeling of visual perception via automatic visual saliency maps for object recognition. Visual saliency represents an efficient way to drive the scene analysis towards particular areas considered ‘of interest’ for a viewer and an efficient alternative to computationally intensive sliding window methods for object recognition. Using saliency maps, we consider biologically inspired independent paths of central and peripheral vision and apply them to fundamental steps of the so-called Bag-of-Words (BoW) paradigm, such as features sampling, pooling and encoding. Our proposal has been evaluated addressing the challenging task of active object recognition, and the results show that our method not only improves the baselines, but also achieves state-of-the-art performance in various datasets at very competitive computational times.
Description
Keywords
Perceptual modeling, Visual saliency, Active object recognition, Foveal and peripheral pathways, Classification, Attention, Features
Bibliographic citation
Pattern Recognition, (2016), v. 56, pp.: 129-141.