Content-aware rate control scheme for HEVC based on static and dynamic saliency detection
High efficiency video coding (HEVC) greatly outperforms previous standards H. 264/AVC in
terms of coding bit rate and video quality. However, it does not take into account the human …
terms of coding bit rate and video quality. However, it does not take into account the human …
Over-sampling strategy-based class-imbalanced salient object detection and its application in underwater scene
One major branch of bottom-up salient object detection methods is machine learning-based
methods which learn to classify salient object (positive) and background (negative) based …
methods which learn to classify salient object (positive) and background (negative) based …
Visual attention, visual salience, and perceived interest in multimedia applications
Y Rai, P Le Callet - Academic Press Library in Signal Processing, Volume …, 2018 - Elsevier
This chapter clarifies the concept and the usage of visual attention in multimedia
applications. Visual attention modeling has been the focus of many research efforts in the …
applications. Visual attention modeling has been the focus of many research efforts in the …
Co-salient object detection based on deep saliency networks and seed propagation over an integrated graph
This paper presents a co-salient object detection method to find common salient regions in a
set of images. We utilize deep saliency networks to transfer co-saliency prior knowledge and …
set of images. We utilize deep saliency networks to transfer co-saliency prior knowledge and …
Two-stage salient object detection based on prior distribution learning and saliency consistency optimization
Y Wu, X Chang, D Chen, L Chen, T Jia - The Visual Computer, 2023 - Springer
Although two-stage methods have favorably improved the accuracy and robustness of
saliency detection, obtaining a saliency map with clear foreground boundaries and fine …
saliency detection, obtaining a saliency map with clear foreground boundaries and fine …
Optimizing multi-graph learning based salient object detection
S Li, C Zeng, Y Fu, S Liu - Signal Processing: Image Communication, 2017 - Elsevier
In this paper, we propose a novel bottom-up saliency detection algorithm to effectively detect
salient objects. Different from most existing methods that are not robust to complex scenes …
salient objects. Different from most existing methods that are not robust to complex scenes …
Salient object detection based on novel graph model
Y Pang, X Yu, Y Wang, C Wu - Journal of Visual Communication and …, 2019 - Elsevier
In this paper, we present a salient object detection method based on novel graph structure.
Given image is segmented into small image regions as basic units, we firstly construct an …
Given image is segmented into small image regions as basic units, we firstly construct an …
Salient object detection via a boundary-guided graph structure
Y Wu, T Jia, Y Pang, J Sun, D Xue - Journal of Visual Communication and …, 2021 - Elsevier
Graph-based salient object detection methods have gained more and more attention
recently. However, existing works fail to separate effectively salient object and background …
recently. However, existing works fail to separate effectively salient object and background …
Self-paced learning-based multi-graphs semi-supervised learning
L Wan, C Dong, X Pei - Multimedia Tools and Applications, 2022 - Springer
Graph-based semi-supervised learning has received considerable attention in machine
learning community. The performance of existing methods highly depends on the input …
learning community. The performance of existing methods highly depends on the input …
FSP: a feedback-based saliency propagation method for saliency detection
Y Pang, X Yu, Y Wu, C Wu - Journal of Electronic Imaging, 2020 - spiedigitallibrary.org
Propagation-based methods have attracted more and more attention in saliency detection.
We present a propagation-based saliency detection framework to overcome some …
We present a propagation-based saliency detection framework to overcome some …