Yi, Y.[Yang],
Ding, J.[Jia],
Lai, J.L.[Jie-Ling],
A novel video salient object extraction method based on visual
attention,
SP:IC(28), No. 1, January 2013, pp. 45-54.
Elsevier DOI
1301
Video object segmentation; Salient object extraction; Visual attention
model; Attention object growing
BibRef
Li, Y.,
Sheng, B.,
Ma, L.,
Wu, W.,
Xie, Z.,
Temporally Coherent Video Saliency Using Regional Dynamic Contrast,
CirSysVideo(23), No. 12, 2013, pp. 2067-2076.
IEEE DOI
1312
Computational efficiency.
Region-based visual dynamic contrast.
BibRef
Han, J.,
He, S.,
Qian, X.,
Wang, D.,
Guo, L.,
Liu, T.,
An Object-Oriented Visual Saliency Detection Framework Based on
Sparse Coding Representations,
CirSysVideo(23), No. 12, 2013, pp. 2009-2021.
IEEE DOI
1312
Computer vision
BibRef
Li, W.T.[Wei-Te],
Chang, H.S.[Haw-Shiuan],
Lien, K.C.[Kuo-Chin],
Chang, H.T.[Hui-Tang],
Wang, Y.F.,
Exploring Visual and Motion Saliency for
Automatic Video Object Extraction,
IP(22), No. 7, 2013, pp. 2600-2610.
IEEE DOI conditional random field; foreground object extraction;
foreground-background region separation; visual saliency
1307
BibRef
Li, W.T.[Wei-Te],
Chang, H.T.[Hui-Tang],
Lyu, H.S.[Hermes Shing],
Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Automatic saliency inspired foreground object extraction from videos,
ICIP12(1089-1092).
IEEE DOI
1302
BibRef
Le, T.N.[Trung-Nghia],
Sugimoto, A.[Akihiro],
Video Salient Object Detection Using Spatiotemporal Deep Features,
IP(27), No. 10, October 2018, pp. 5002-5015.
IEEE DOI
1808
BibRef
Earlier:
Contrast Based Hierarchical Spatial-Temporal Saliency for Video,
PSIVT15(734-748).
Springer DOI
1602
feature extraction, image colour analysis, image representation,
image segmentation, object detection, random processes,
video object segmentation
BibRef
Chen, C.,
Li, S.,
Qin, H.,
Pan, Z.,
Yang, G.,
Bilevel Feature Learning for Video Saliency Detection,
MultMed(20), No. 12, December 2018, pp. 3324-3336.
IEEE DOI
1812
feature extraction, learning (artificial intelligence),
Markov processes, object detection, video signal processing,
Video Saliency Detection
BibRef
Wen, H.F.[Hong-Fa],
Zhou, X.F.[Xiao-Fei],
Sun, Y.[Yaoqi],
Zhang, J.Y.[Ji-Yong],
Yan, C.G.[Cheng-Gang],
Deep fusion based video saliency detection,
JVCIR(62), 2019, pp. 279-285.
Elsevier DOI
1908
Video saliency, Convolutional network, Feature integration, Boundary
BibRef
Patil, P.W.,
Murala, S.,
MSFgNet:
A Novel Compact End-to-End Deep Network for Moving Object Detection,
ITS(20), No. 11, November 2019, pp. 4066-4077.
IEEE DOI
1911
Videos, Object detection, Estimation, Feature extraction, Lighting,
Image color analysis, Saliency detection, Background estimation, CNN
BibRef
Patil, P.W.,
Thawakar, O.,
Dudhane, A.,
Murala, S.,
Motion Saliency Based Generative Adversarial Network for Underwater
Moving Object Segmentation,
ICIP19(1565-1569)
IEEE DOI
1910
Underwater medium, motion saliency,
Generative adversarial network, frame segmentation
BibRef
Maczyta, L.[Léo],
Bouthemy, P.[Patrick],
Meur, O.L.[Olivier Le],
CNN-based temporal detection of motion saliency in videos,
PRL(128), 2019, pp. 298-305.
Elsevier DOI
1912
Motion saliency, Temporal detection, CNN-based classification,
Dominant motion estimation, Video analysis
BibRef
Xu, M.,
Liu, B.,
Fu, P.,
Li, J.,
Hu, Y.H.,
Video Saliency Detection via Graph Clustering With Motion Energy and
Spatiotemporal Objectness,
MultMed(21), No. 11, November 2019, pp. 2790-2805.
IEEE DOI
1911
Spatiotemporal phenomena, Visualization, Dynamics, Proposals,
Feature extraction, Fuses, Video saliency,
graph clustering
BibRef
Chen, C.,
Wang, G.,
Peng, C.,
Zhang, X.,
Qin, H.,
Improved Robust Video Saliency Detection Based on Long-Term
Spatial-Temporal Information,
IP(29), No. 1, 2020, pp. 1090-1100.
IEEE DOI
1911
Saliency detection, Deep learning, Quality assessment, Trajectory,
Computational modeling, Color, Training, Video saliency detection,
long-term information revealing
BibRef
Lai, Q.,
Wang, W.,
Sun, H.,
Shen, J.,
Video Saliency Prediction Using Spatiotemporal Residual Attentive
Networks,
IP(29), No. 1, 2020, pp. 1113-1126.
IEEE DOI
1911
Visualization, Computational modeling, Spatiotemporal phenomena,
Dynamics, Task analysis, Data models, Predictive models,
video saliency
BibRef
Fang, Y.,
Ding, G.,
Li, J.,
Fang, Z.,
Deep3DSaliency: Deep Stereoscopic Video Saliency Detection Model by
3D Convolutional Networks,
IP(28), No. 5, May 2019, pp. 2305-2318.
IEEE DOI
1903
convolutional neural nets, deconvolution, feature extraction,
image sequences, object detection, optimisation,
3D convolutional neural networks
BibRef
Fang, Y.M.[Yu-Ming],
Zhang, X.Q.[Xiao-Qiang],
Yuan, F.N.[Fei-Niu],
Imamoglu, N.[Nevrez],
Liu, H.[Haiwen],
Video saliency detection by gestalt theory,
PR(96), 2019, pp. 106987.
Elsevier DOI
1909
Visual attention, Video saliency detection, Gestalt theory,
Uncertainty weighting, Spatiotemporal saliency
BibRef
Wang, Q.[Qiong],
Zhang, L.[Lu],
Zou, W.B.[Wen-Bin],
Kpalma, K.[Kidiyo],
Salient video object detection using a virtual border and guided
filter,
PR(97), 2020, pp. 106998.
Elsevier DOI
1910
Video salient object detection, Distance transform,
Guided filter, Global motion
BibRef
Wu, Z.,
Su, L.,
Huang, Q.,
Learning Coupled Convolutional Networks Fusion for Video Saliency
Prediction,
CirSysVideo(29), No. 10, October 2019, pp. 2960-2971.
IEEE DOI
1910
computer vision, convolutional neural nets, feature extraction,
image motion analysis, inference mechanisms,
fully convolutional network
BibRef
Li, J.,
Fu, K.,
Zhao, S.,
Ge, S.,
Spatiotemporal Knowledge Distillation for Efficient Estimation of
Aerial Video Saliency,
IP(29), No. 1, 2020, pp. 1902-1914.
IEEE DOI
1912
Computational modeling, Redundancy, Estimation, Visualization,
Data models, Spatiotemporal phenomena, Drones,
aerial video
BibRef
Zhang, K.[Kao],
Chen, Z.Z.[Zhen-Zhong],
Video Saliency Prediction Based on Spatial-Temporal Two-Stream
Network,
CirSysVideo(29), No. 12, December 2019, pp. 3544-3557.
IEEE DOI
1912
Feature extraction, Predictive models, Streaming media,
Visualization, Spatiotemporal phenomena, Computational modeling,
deep learning
BibRef
Kavak, Y.[Yasin],
Erdem, E.[Erkut],
Erdem, A.[Aykut],
Hedging static saliency models to predict dynamic saliency,
SP:IC(81), 2020, pp. 115694.
Elsevier DOI
1912
Dynamic saliency, Hedge algorithm,
Decision theoretic online learning, Feature integration
BibRef
Hoo, W.L.,
Chan, C.S.,
Anisotropic Partial Differential Equation Based Video Saliency
Detection,
ICIP18(2311-2315)
IEEE DOI
1809
Saliency detection, Image color analysis, Tensile stress,
Feature extraction, Task analysis, Discrete cosine transforms,
partial differential equation
BibRef
Zhang, L.[Long],
Liu, Y.J.[Yu-Jun],
Han, S.D.[Shou-Dong],
Video segmentation based on strong target constrained video saliency,
ICIVC17(356-360)
IEEE DOI
1708
Adaptive optics, Image color analysis, Image segmentation,
Optical imaging, Robustness, Target tracking,
full-connected conditional random field, object proposal,
super-pixel, video saliency, video, segmentation
BibRef
Wei, L.,
Wang, F.,
Li, X.,
Wu, F.,
Xiao, J.,
Graph-theoretic spatiotemporal context modeling for video saliency
detection,
ICIP17(4197-4201)
IEEE DOI
1803
Atomic layer deposition, Context modeling, Manifolds,
Saliency detection, Spatiotemporal phenomena,
Video saliency
BibRef
Hu, Y.T.[Yuan-Ting],
Huang, J.B.[Jia-Bin],
Schwing, A.G.[Alexander G.],
VideoMatch: Matching Based Video Object Segmentation,
ECCV18(VIII: 56-73).
Springer DOI
1810
BibRef
And:
Unsupervised Video Object Segmentation Using Motion Saliency-Guided
Spatio-Temporal Propagation,
ECCV18(I: 813-830).
Springer DOI
1810
BibRef
Xue, K.[Kang],
Wang, X.[Xiying],
Ma, G.Y.[Geng-Yu],
Wang, H.T.[Hai-Tao],
Nam, D.[Dong_Kyung],
A video saliency detection method based on spatial and motion
information,
ICIP15(412-416)
IEEE DOI
1512
Classification; Motion; Saliency; Video
BibRef
Lee, T.[Teahyung],
Hwangbo, M.[Myung],
Alan, T.[Tanfer],
Tickoo, O.[Omesh],
Iyer, R.[Ravishankar],
Low-complexity HOG for efficient video saliency,
ICIP15(3749-3752)
IEEE DOI
1512
low-complexity HOG; video saliency
BibRef
Hiratani, A.,
Nakashima, R.,
Matsumiya, K.,
Kuriki, I.,
Shioiri, S.,
Considerations of Self-Motion in Motion Saliency,
ACPR13(783-787)
IEEE DOI
1408
feature extraction
BibRef
Xia, Y.[Yang],
Hu, R.M.[Rui-Min],
Huang, Z.K.[Zhen-Kun],
Su, Y.[Yin],
A novel method for generation of motion saliency,
ICIP10(4685-4688).
IEEE DOI
1009
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Spatio-Temporal Motion Segmentation, Flow Based Segmentation .