Abstract
Fixation as representation of one viewer’s attention are very intuitive to reflect the viewer’s observation procedure. The viewer’s observation behavior can be further revealed by analyzing fixations features. In this paper, we propose a fixation based personalized salient object segmentation method involving personal observation behavior learning. Concretely, we design three neural networks and deploy a meta-learning method. The first network is a base segmentation network that can be converted into a meta-segmentation network by meta-learning. The meta- segmentation network can learn one viewer’s observation behavior from only one sample and then generates the viewer’s segmentation network to segment the other samples. Moreover, a fusion network plays an important role in alleviating an unsuitable transmission problem and generating a final segmentation result. The experimental results demonstrate the reasonability of our observation behavior learning and the effectiveness of the three proposed neural networks.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.