Selected Publication

创建时间:  2023年04月18日 15:24  郑金丹   浏览次数:   

Selected Publications:

2021

•G. Li, Z. Liu, R. Shi, Z. Hu, W. Wei, Y. Wu, M. Huang, and H. Ling. “Personal Fixations-Based Object Segmentation with Object Localization and Boundary Preservation," in IEEE Transactions on Image Processing, vol. 30, pp. 1461-1475, Jan. 2021.
•W. Wei, Z. Liu, L. Huang, Z. Wang, W. Chen, T. Zhang, J. Wang, and L. Xu. “Identify autism spectrum disorder via dynamic filter and deep spatiotemporal feature extraction,” Signal Processing: Image Communication, vol. 95, pp. 116195, May. 2021.
•Z. Wang, Z. Liu, W. Wei, and H. Duan. “SalED: Saliency prediction with a pithy encoder-decoder architecture sensing local and global information”, Image and Vision Computing, vol. 109, pp. 104149, May. 2021.
•G. Li, Z. Liu, M. Chen, Z. Bai, W. Wei, W. Lin, and H. Ling. “Hierarchical Alternate Interaction Network for RGB-D Salient Object Detection," in IEEE Transactions on Image Processing, vol. 30, pp. 3528-3542, Mar. 2021.

2020

•L.Huang, W. Wei, Z. Liu*, T. Zhang, J. Wang, L. Xu, W. Chen and O. Le Meur,“Effective schizophrenia recognition using discriminative eye movement features and model-metric based features”,Pattern recognition letters, 2020, pp.608-616.
•W. Wei, Z. Liu, L. Huang, A. Nebout, O. Le Meur, T. Zhang, J. Wang, and L. Xu,“Predicting atypical visual saliency for autism spectrum disorder via scale-adaptive inception module and discriminative region enhancement lose" Neurocomputing,doi.org/10.1016/j.neucom.2020.06.125.
•G. Li, Z. Liu, L. Ye,Y. Wang and H. Ling,“Cross-Modal Weighting Network for RGB-D Salient Object Detection”,European Conference on Computer Vision (ECCV), 2020.
•G. Li, Z. Liu, and H. Ling,“ICNet: Information Conversion Network for RGB-D Based Salient Object Detection”,IEEE Transactions on Image Processing, doi: 10.1109/TIP.2020.2976689, 2020.
•M. Chen, Z. Liu, L. Ye and Y. Wang,“Attentional coarse-and-fine generative adversarial networks for image inpainting”,Neurocomputing Volume 405, 10 September 2020, Pages 259-269.
•J. Ren, Z. Liu, G. Li, X. Zhou, C. Bai, and G. Sun,“Co-saliency detection using collaborative feature extraction and high-to-low feature integration”,International Conference on Multimedia and Expo (ICME), London, United Kingdom, Jul. 2020.
•Q. Jiao, Z. Liu, G. Li, L, Ye and Y. Wang,“Fine-grained image classification with coarse and fine labels on one-shot learning",International Conference on Multimedia and Expo Workshop(ICMEW), London, United Kingdom, Jul. 2020.
•Y. Wu, Z. Liu, and X. Zhou,“Saliency detection using adversarial learning networks”,Journal of Visual Communication and Image Representation, doi.org/10.1016/j.jvcir.2020.102761, 2020.


2019

•.L. Ye, M. Rochan, Z. Liu and Y. Wang,“Cross-Modal Self-Attention Network for Referring Image Segmentation”,Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 2019, pp.10502-10511,
•J. Ren, Z. Liu, X. Zhou, C. Bai and G. Sun,“Co-saliency detection via integration of multi-layer convolutional features and inter-image propagation”,Neurocomputing, doi.org/10.1016/j.neucom.2019.09.010.
•G. Li, Z. Liu, R. Shi, and W. Wei,“Constrained Fixation Point based Segmentation via Deep Neural Network”,Neurocomputing, vol. 368, pp. 180-187, Nov. 2019.
•W. Wei, Z. Liu, L. Huang, A. Nebout, and O. Le Meur,“Saliency Prediction via Multi-Level Features and Deep Supervision for Children with Autism Spectrum Disorder”,IEEE International Conference Multimedia Expo Grand Challenge, 2019, pp.621–624.
•M. Huang, Z. Liu, L. Ye, X. Zhou and Y. Wang,"Saliency detection via multi-level integration and multi-scale fusion neural networks",Neurocomputing, vol. 364, pp. 310-321, Oct. 2019.
•Q. Jiao, Z. Liu, L. Ye and Y. Wang,"Weakly labeled fine-grained classification with hierarchy relationship of fine and coarse labels",Journal of Visual Communication and Image Representation, vol. 63, Aug. 2019.
•Y. Ding, Z. Liu, M. Huang, R Shi, and X. Wang,"Depth-aware saliency detection using convolutional neural networks",Journal of Visual Communication and Image Representation, vol.61, pp.1-9, May. 2019.
•G. Li, Z. Liu, and X. Zhou,"Effective Online Refinement for Video Object Segmentation",Multimedia Tools and Applications, doi: 10.1007/s11042-019-08146-3, Sept. 2019.
•X. Liu, Z. Liu, and Q. Jiao, O. L. Meur and W.-L. Zhao,"Saliency-aware inter-image color transfer for image manipulation",Multimedia Tools and Applications, vol.78, pp.21629-21644, Aug. 2019.
•X. Liu, Z. Liu, X. Zhou and M. Chen,"SALIENCY-GUIDED IMAGE STYLE TRANSFER",ICMEWorkshop, July. 2019.


2018

•X. Zhou, Z. Liu, C. Gong, and W. Liu, “Improving video saliency detection via localized estimation and spatiotemporal refinement,” IEEE Transactions on Multimedia, vol. 20, no. 11, pp. 2993-3007, Nov. 2018.
•L. Shen, K. Li, G. Feng, P. An, and Z. Liu, “Efficient intra mode selection for depth-map coding utilizing spatiotemporal, inter-component and inter-view correlations in 3D-HEVC,” IEEE Transactions on Image Processing, vol. 27, no. 9, pp. 4195-4206, Sept. 2018.
•H. Du, Z. Liu, and R. Shi, “Salient object segmentation based on depth-aware Image layering,” Multimedia Tools and Applications, DOI: 10.1007/s11042-018-6736-4, Sept. 2018.
•Y. Xie, Z. Liu, X. Zhou, W. Liu, and X. Zou, “Video co-segmentation based on directed graph,” Multimedia Tools and Applications, DOI: 10.1007/s11042-018-6614-0, Aug. 2018.
•X. Zhou, Z. Liu, C. Gong, G. Li, and M. Huang, “Video saliency detection using deep convolutional neural networks,” Chinese Conference on Pattern Recognition and Computer Vision (PRCV), Guangzhou, China, Nov. 2018.
•L. Wu, Z. Liu, and R. Shi, “Multiple kernel boosting based two-level RGBD image co-segmentation,” IEEE International Conference on Multimedia Big Data (BigMM), Xi’an, China, Sept. 2018.
•X. Sun, Y. Wang, T. Ren, Z. Liu, Z. Zha, and G. Wu, “Object trajectory proposal via hierarchical volume grouping,” ACM International Conference on Multimedia Retrieval (ICMR), Yokohama, Japan, Jun. 2018, pp. 344-352.
•X. Zhou, Z. Liu, K. Li, and G. Sun, “Video saliency detection via bagging-based prediction and spatiotemporal propagation,” Journal of Visual Communication and Image Representation, vol. 51, pp. 131-143, Feb. 2018.
•L. Ye, Z. Liu, and Y. Wang, “Learning semantic segmentation with diverse supervision,” IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, USA, Mar. 2018, pp. 1461-1469.
•L. Wu, Z. Liu, H. Song, and O. Le Meur, “RGBD co-saliency detection via multiple kernel boosting and fusion,” Multimedia Tools and Applications, vol. 77, no. 16, pp. 21185-21199, Aug. 2018.
•T. Wu, Z. Liu, X. Zhou, and K. Li, “Spatiotemporal salient object detection by integrating with objectness,” Multimedia Tools and Applications, vol. 77, no. 15, pp. 19481-19498, Aug. 2018.
•L. Li, Z. Liu, and J. Zhang, “Unsupervised image co-segmentation via guidance of simple images,” Neurocomputing, vol. 275, pp. 1650-1661, Jan. 2018.
•J. Ren, Z. Liu, X. Zhou, G. Sun, and C. Bai, “Saliency integration driven by similar images,” Journal of Visual Communication and Image Representation, vol. 50, pp. 227-236, Jan. 2018.


2017

•X. Zhang, C. Zhu, H. Wu, Z. Liu, and Y. Xu, “An imbalance compensation framework for background subtraction,” IEEE Transactions on Multimedia, vol. 19, no. 11. pp. 2425-2438, Nov. 2017.
•O. Le Meur, A. Coutrot, Z. Liu, P. Rämä, A. Le Roch, and A. Helo, “Visual attention saccadic models learn to emulate gaze patterns from childhood to adulthood,” IEEE Transactions on Image Processing, vol. 26, no. 10, pp. 4777-4789, Oct. 2017.
•H. Song, Z. Liu, H. Du, G. Sun, O. Le Meur, and T. Ren, “Depth-aware salient object detection and segmentation via multiscale discriminative saliency fusion and bootstrap learning,” IEEE Transactions on Image Processing, vol. 26, no. 9, pp. 4204-4216, Sep. 2017.
•L. Ye, Z. Liu, L. Li, L. Shen, C. Bai, and Y. Wang, “Salient object segmentation via effective integration of saliency and objectness,” IEEE Transactions on Multimedia, vol. 19, no. 8. pp. 1742-1756, Aug. 2017.
•C. Ge, N. Kasabov, Z. Liu, and J. Yang, “A spiking neural network model for obstacle avoidance in simulated prosthetic vision,” Information Sciences, vol. 399, pp. 30-42, Aug. 2017.
•H. Cheng, J. Zhang, Q. Wu, P. An, and Z. Liu, “Stereoscopic visual saliency prediction based on stereo contrast and stereo focus,” EURASIP Journal on Image and Video Processing, vol. 2017, article 61, Sep. 2017.
•L. Shen, P. An, and Z. Liu, “Context-adaptive based CU processing for 3D-HEVC,” PLoS One, vol. 12, no. 2, e0171018, Feb. 2017.
•W. Liu, X. Chen, C. Shen, Z. Liu, and J. Yang, “Semi-global weighted least squares in image filtering,” International Conference on Computer Vision (ICCV), pp. 5861-5869, Venice, Italy, Oct. 2017.
•J. Chen, C. Bai, L. Huang, Z. Liu, and S. Chen, “Visual saliency fusion based multi-feature for semantic image retrieval,” Chinese Conference on Computer Vision (CCCV), pp. 126-136, Tianjin, China, Oct. 2017.
•T. Maugey, O. Le Meur, and Z. Liu, “Saliency-based navigation in omnidirectional image,” IEEE International Workshop on Multimedia Signal Processing (MMSP), pp. 1-6, London-Luton, U.K., Oct. 2017.
•L. Ye, Z. Liu, and Y. Wang, “Depth-aware object instance segmentation,” International Conference on Image Processing (ICIP), pp. 325-329, Beijing, Sep. 2017.
•O. Le Meur, A. Coutrot, A. Le Roch, A. Helo, P. Rama, and Z. Liu, “Age-dependent saccadic models for predicting eye movements,” International Conference on Image Processing (ICIP), pp. 3740-3744, Beijing, Sep. 2017.
•W. Shan, G. Sun, X. Zhou, and Z. Liu, “Two-stage transfer learning of end-to-end convolutional neural networks for webpage saliency prediction,” International Conference on Intelligence Science and Big Data Engineering (IScIDE), pp. 316-324, Dalian, China, Sep. 2017.
•G. Sun, J. Ren, Z. Liu, and W. Shan, “Two-stage saliency fusion for object segmentation,” International Conference on Image and Graphics (ICIG), pp. 139-148, Shanghai, China, Sep. 2017.
•O. Le Meur, A. Coutrot, Z. Liu, A. Le Roch, A. Helo, and P. Rama, “Your gaze betrays your age,” European Signal Processing Conference (EUSIPCO), pp. 1942-1946, Kos Island, Greece, Aug. 2017.


2016

•H. Song, Z. Liu, Y. Xie, L. Wu, and M. Huang, “RGBD co-saliency detection via bagging-based clustering,” IEEE Signal Processing Letters, vol. 23, no. 12, pp. 1722-1726, Dec. 2016.
•H. Du, Z. Liu, H. Song, L. Mei and Z. Xu, “Improving RGBD saliency detection using progressive region classification and saliency fusion,” IEEE Access, vol. 4, pp. 8987-8994, Dec. 2016.
•X. Zhou, Z. Liu, G. Sun, and X. Wang, “Adaptive saliency fusion based on quality assessment,” Multimedia Tools and Applications, DOI: 10.1007/s11042-016-4093-8, Nov. 2016.
•X. Wang, Y. Zhao, Z. Liu, Y. Jin, and X. Zhu, “Multi-scale deep residual networks for fine-grained image classification,” International Forum of Digital TV and Wireless Multimedia Communication, pp. 205-217, Shanghai, China, Nov. 2016.
•W. Shan, G. Sun, and Z. Liu, “Webpage image saliency prediction via adaptive SVM,” International Forum of Digital TV and Wireless Multimedia Communication, pp. 128-136, Shanghai, China, Nov. 2016.
•X. Geng, L. Shen, P. An, and Z. Liu, “Using independent component analysis and binocular combination for stereoscopic image quality assessment,” IEEE Visual Communications and Image Processing (VCIP) Conference, pp. 1-4, Chengdu, China, Nov. 2016.
•L. Shen, Q. Hu, Z. Liu, and P. An, “A new rate control algorithm based on region of interest for HEVC,” Pacific-Rim Conference on Multimedia (PCM), Part II, LNCS 9917, pp. 571-579, Xi’an, China, Sep. 2016.
•Z. Liu, J. Li, L. Ye, G. Sun, and L. Shen,  “Saliency detection for unconstrained videos using superpixel-level graph and spatiotemporal propagation,” IEEE Transactions on Circuits and Systems for Video Technology, doi.: 10.1109/TCSVT.2016.2595324, Jul. 2016.
•G. Sun, Y. Dong, X. Zhou, and Z. Liu,  “Facial descriptor for Kinect depth using inner–inter-normal components local binary patterns and tensor histograms,” Machine Vision and Applications, doi.: 10.1007/s00138-016-0783-5, Jun. 2016.
•L. Ye, Z. Liu, X. Zhou, L. Shen, and J. Zhang,  “Saliency detection via similar image retrieval,” IEEE Signal Processing Letters, vol. 23, no. 6, pp. 838-842, Jun. 2016.
•X. Zhou, Z. Liu, G. Sun, L. Ye, and X. Wang,  “Improving saliency detection via multiple kernel boosting and adaptive fusion,” IEEE Signal Processing Letters, vol. 23, no. 4, pp. 517-521, Apr. 2016.
•H. Song, Z. Liu, H. Du, and G. Sun,  “Depth-aware saliency detection using discriminative saliency fusion,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1626-1630, Shanghai, China, Mar. 2016.


2015

• W.Zou, Z.Liu, K.Kpalma, J.Ronsin, Y.Zhao, and N. Komodakis, “Unsupervised joint salient region detection and object segmentation,” IEEE Transactions on Image Processing, vol.24, no.11, pp.3858-3873, Nov.2015.
• L.Ye, Z.Liu, J.Li, W.Zhao and L.Shen, “Co-saliency detection via co-salient object discovery and recovery,” IEEE Signal Processing Letters, vol.22,no.11,pp.2073-2077, Nov.2015.
•Z. Liu, O. Le Meur, A. Borji and H. Li,  “Special issue on recent advances in saliency models, applications and evaluations,” Signal Processing: Image Communication, vol. 38, pp. 1-2, Oct. 2015.
•H. Song, Z. Liu, H. Du, G. Sun, and C. Bai,  “Saliency detection for RGBD images,” International Conference on Internet Multimedia Computing and Service (ICIMCS), article no. 72, Zhangjiajie, China, Aug. 2015.
•L. Ye, Z. Liu, and L. Li,  “Evaluation on fusion of saliency and objectness for salient object segmentation,” International Conference on Internet Multimedia Computing and Service (ICIMCS), article no. 18, Zhangjiajie, China, Aug. 2015.
•X. Fan, Z. Liu, and L. Ye,  “Salient object segmentation from stereoscopic images,” 10th IAPR-TC15 Workshop on Graph-based Representation in Pattern Recognition (GbRPR), pp. 272-281, Beijing, May 2015.
• Y. Li, K. Fu, Z. Liu, and J. Yang, “Efficient saliency-model-guided visual co-saliency detection,” IEEE Signal Processing Letters, vol. 22, no. 5, pp. 588-592, May 2015.
• J. Li, Z. Liu, X. Zhang, O. Le Meur, and L. Shen,  “Spatiotemporal saliency detection based on superpixel-level trajectory,” Signal Processing: Image Communication, vol. 38, pp. 100-114, Oct. 2015.
• L. Shen, Z. Zhang, X. Zhang, P. An, and Z. Liu, “Fast TU size decision algorithm for HEVC encoders using Bayesian theorem detection,” Signal Processing: Image Communication, vol. 32, pp. 121-128, Mar. 2015.
• O. Le Meur, and Z. Liu,  “Saccadic model of eye movements for free-viewing condition,” Vision Research, vol. 116, part B, pp. 152-164, Nov. 2015.
• C. Bai, J. Zhang, Z. Liu, and W. Zhao, “K-means based histogram using multiresolution feature vectors for color texture database retrieval,” Multimedia Tools and Applications, vol. 74, no. 4, pp. 1469-1488, Feb. 2015.


2014

• O. Le Meur, and Z. Liu, “Saliency aggregation: Does unity make strength?,” Asian Conference on Computer Vision (ACCV), Singapore, Nov. 2014.
• G. Sun, and Z. Liu, “Robust blurred face recognition using sample-wise kernel estimation and random compressed multi-scale local binary pattern histograms,” International Conference on Image Processing (ICIP), pp. 333-337, Paris, Oct. 2014.
• L. Shen, Z. Zhang, and Z. Liu, “Effective CU size decision for HEVC intracoding,” IEEE Transactions on Image Processing, vol. 23, no. 10, pp. 4232-4241, Oct. 2014.
• L. Shen, Z. Zhang, and Z. Liu, “Adaptive inter-mode decision for HEVC jointly utilizing inter-level and spatiotemporal correlations,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 24, no. 10, pp. 1709-1722, Oct. 2014.
• Z. Liu, X. Zhang, S. Luo, and O. Le Meur, “Superpixel-based spatiotemporal saliency detection,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 24, no. 9, pp. 1522-1540, Sep. 2014.
• X. Fan, Z. Liu, and G. Sun, "Salient region detection for stereoscopic images," International Conference on Digital Signal Processing (DSP), pp. 454-458, Hong Kong, Aug. 2014.
• X. Zhang, Z. Liu, H. Li, X. Zhao, and P. Zhang, “Statistical background subtraction based on imbalanced learning,” International Conference on Multimedia and Expo (ICME), pp. 1-6, Chengdu, Jul. 2014.
• L. Li, Z. Liu, W. Zou, X. Zhang, and O. Le Meur, “Co-saliency detection based on region-level fusion and pixel-level refinement,” International Conference on Multimedia and Expo (ICME), pp. 1-6, Chengdu, Jul. 2014.
• Z. Liu, W. Zou, and O. Le Meur, “Saliency tree: A novel saliency detection framework,” IEEE Transactions on Image Processing, vol. 23, no. 5, pp. 1937-1952, May 2014.
• L. Shen, P. An, Z. Liu, and Z. Zhang, “Low complexity depth coding assisted by coding information from color video,” IEEE Transactions on Broadcasting, vol. 60, no. 1, pp. 128-133, Mar. 2014.
• Z. Liu, W. Zou, L. Li, L. Shen, and O. Le Meur, “Co-saliency detection based on hierarchical segmentation,” IEEE Signal Processing Letters, vol. 21, no. 1, pp. 88-92, Jan. 2014.


2013

• W. Zou, K. Kpalma, Z. Liu, and J. Ronsin, “Segmentation driven low-rank matrix recovery for saliency detection,” British Machine Vision Conference (BMVC), Bristol, United Kingdom, article 79, Sep. 2013.
• X. Zhang, J. Cheng, Z. Liu, and J. Yang, “Cost-sensitive background subtraction,” International Conference on Image Processing (ICIP), Melbourne, Australia, pp. 3336-3339, Sep. 2013.
• Z. Liu, O. Le Meur, and S. Luo, “Superpixel-based saliency detection,” International Workshop on Image and Audio Analysis for Multimedia Interactive Services (WIAMIS), Paris, France, article 6616119, Jul. 2013.
• H. Du, Z. Liu, J. Jiang, and L. Shen, “Stretchability-aware block scaling for image retargeting,” Journal of Visual Communication and Image Representation, vol. 24, no. 4, pp. 499-508, May 2013.
• L. Zha, Z. Liu, S. Luo, and L. Shen, “A novel region merging based image segmentation approach for automatic object extraction,” IEEE International Symposium on Circuits and Systems (ISCAS), Beijing, China, pp. 970-973, 19-23 May 2013.
• L. Shen, Z. Liu, and Z. Zhang, “A novel H.264 rate control algorithm with consideration of visual attention,” Multimedia Tools and Applications, vol. 63, no. 3, pp. 709-727, Apr. 2013.
• Z. Liu, O. Le Meur, S. Luo, and L. Shen, “Saliency detection using regional histograms,” Optics Letters, vol. 38, no. 5, pp. 700-702, Mar. 2013.
• L. Shen, Z. Liu, X. Zhang, W. Zhao, and Z. Zhang, “An effective CU size decision method for HEVC encoders,” IEEE Transactions on Multimedia, vol. 15, no. 2. pp. 465-470, Feb. 2013.


2012

• Z. Liu, R. Shi, L. Shen, Y. Xue, K. N. Ngan, and Z. Zhang, “Unsupervised salient object segmentation based on kernel density estimation and two-phase graph cut,” IEEE Transactions on Multimedia, vol. 14, no. 4, pp. 1275-1289, Aug. 2012.
• L. Shen, Z. Zhang, and Z. Liu, “Inter mode selection for depth map coding in 3D video,” IEEE Transactions on Consumer Electronics, vol. 58, no. 3, pp. 926-931, Aug. 2012.
• R. Shi, Z. Liu, H. Du, X. Zhang, and L. Shen, “Region diversity maximization for salient object detection,” IEEE Signal Processing Letters, vol. 19, no. 4, pp. 215–218, Apr. 2012.


2011

• R. Shi, Z. Liu, Y. Xue, and X. Zhang, “Interactive object segmentation using iterative adjustable graph cut,” Visual Communications and Image Processing (VCIP) Conference, Tainan, Taiwan, Nov. 2011.
• L. Shen, Z. Liu, P. An, R. Ma, and Z. Zhang, “Low-complexity mode decision for MVC,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 6, pp. 837-843, Jun. 2011.
• Y. Xue, Z. Liu, and Shi Ran, “Saliency detection using multiple region-based features,” Optical Engineering, vol. 50, no. 5, pp. 057008, May 2011.
• Z. Liu, Y. Xue, H. Yan, and Z. Zhang, “Efficient saliency detection based on Gaussian models,” IET Image Processing, vol. 5, no. 2, pp. 122-131, Mar. 2011.
• Z. Liu, L. Shen, and Z. Zhang, “Unsupervised image segmentation based on analysis of binary partition tree for salient object extraction,” Signal Processing, vol. 91, no. 2, pp. 290-299, Feb. 2011.


2010

• R. Shi, Z. Liu, Y. Xue, “Salient object segmentation based on graph cut,” International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS), Chengdu, China, pp. 321-324, Dec. 2010.
• X. Zhang, Z. Liu, J. Yang, “Moving object segmentation based on new likelihood functions”, International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS), Chengdu, China, pp. 313-316, Dec. 2010.
• L. Shen, Y. Sun, Z. Liu, and Z. Zhang, “Efficient SKIP mode detection for coarse grain quality scalable video coding,” IEEE Signal Processing Letters, vol. 17, no. 10, pp. 887-890, Oct. 2010.
• Z. Liu, Y. Xue, L. Shen, and Z. Zhang, “Nonparametric saliency detection using kernel density estimation,” International Conference on Image Processing (ICIP), Hong Kong, pp. 253-256, Sept. 2010.
• L. Shen, Z. Liu, P. An, R. Ma, and Z. Zhang, “An adaptive early termination of mode decision using inter-layer correlation in scalable video coding,” International Conference on Image Processing (ICIP), Hong Kong, pp. 4229-4232, Sept. 2010.
• L. Shen, Z. Liu, P. An, R. Ma, and Z. Zhang, “Fast mode decision for scalable video coding utilizing spatial and interlayer correlation,” Journal of Electronic Imaging, vol. 19, no. 3, pp. 033010, Aug. 2010.
• Z. Liu, L. Wang, L. Shen, and Z. Zhang, “Unsupervised salient object segmentation from color images,” SPIE Conference on Visual Communications and Image Processing (VCIP), Huangshan, China, pp. 77441Q, Jul. 2010.
• L. Shen, Z. Liu, T. Yan, Z. Zhang, and P. An, “View-adaptive motion estimation and disparity estimation for low complexity multiview video coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 20, no. 6, pp. 925-930, Jun. 2010.
• Z. Liu, W. Li, L. Shen, Z. Han, and Z. Zhang, “Automatic segmentation of focused objects from images with low depth of field,” Pattern Recognition Letters, vol. 31, no. 7, pp. 572-581, May 2010.
• L. Shen, Z. Liu, T. Yan, Z. Zhang, and P. An, “Early SKIP mode decision for MVC using inter-view correlation,” Signal Processing: Image Communication, vol. 25, no. 2, pp. 88-93, Feb. 2010.
• Z. Liu, H. Yan, L. Shen, K. Ngan, and Z. Zhang, “Adaptive image retargeting using saliency-based continuous seam carving,” Optical Engineering, vol. 49, no. 1, pp. 017002, Jan. 2010.


2009

• L. Shen, Z. Liu, S. Liu, Z. Zhang, and P. An, “Selective disparity estimation and variable size motion estimation based on motion homogeneity for multi-view coding,” IEEE Transactions on Broadcasting, vol. 55, no. 4, pp. 761-766, Dec. 2009.
• L. Shen, T. Yan, Z. Liu, Z. Zhang, P. An, and L. Yang, “Fast mode decision for multiview video coding,” International Conference on Image Processing (ICIP),Cairo, Egypt, pp. 2953-2956, Nov. 2009.
• X. Zhang, J. Yang, and Z. Liu, “Foreground segmentation based on tracking,” Optical Engineering, vol. 48, no. 10, pp. 107203, Oct. 2009.
• L. Shen, G. Feng, Z. Liu, Z. Zhang, and P. An, “Macroblock-level adaptive search range algorithm for motion estimation in multiview video coding,” Journal of Electronic Imaging, vol. 18, no. 3, pp. 033003, Jul. 2009.
• Z. Liu, H. Yan, L. Shen, Y. Wang, and Z. Zhang, “A motion attention model based rate control algorithm for H.264/AVC,” IEEE/ACIS International Conference on Computer and Information Science (ICIS), Shanghai, China, pp. 568-573, Jun. 2009.
• Z. Han, Z. Liu, Z. Zhang, Y. Lu, W. Li, and H. Yan, “Salient object extraction based on region saliency ratio,” IEEE/ACIS International Conference on Computer and Information Science (ICIS), Shanghai, China, pp. 611-615, Jun. 2009.
• L. Shen, Z. Liu, Z. Zhang, and X. Shi, “Selective VS-MRF-ME and intra coding in H.264 based on spatiotemporal continuity of motion field,” Signal Processing: Image Communication, vol. 24, no. 5, pp. 405-414, May 2009.
• X. Zhang, J. Yang, and Z. Liu, “Performance evaluation of foreground modeling in moving foreground segmentation,” Optical Engineering, vol. 48, no. 3, pp. 030505, Mar. 2009.
• Z. Liu, L. Shen, and Z. Zhang, “An efficient intermode decision algorithm based on motion homogeneity for H.264/AVC,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 19, no. 1, pp. 128-132, Jan. 2009.
• L. Shen, Z. Liu, Z. Zhang, and X. Shi, “Frame-level bit allocation based on incremental PID algorithm and frame complexity estimation,” Journal of Visual Communication and Image Representation, vol. 20, no. 1, pp. 28-34, Jan. 2009.


2008

• L. Shen, Z. Liu, and Z. Zhang, “Fast multiframe selection algorithm based on spatial-temporal characteristics of motion field,” Journal of Electronic Imaging, vol. 17, no. 4, pp. 043004, Dec. 2008.
• L. Shen, Z. Liu, Z. Zhang, and X. Shi, “Fast inter mode decision using spatial property of motion field,” IEEE Transactions on Multimedia, vol. 10, no. 6, pp. 1208-1214, Oct. 2008.
• Z. Liu, W. Li, X. Zhang, and J. Yang, “Efficient face segmentation based on face attention model and seeded region merging,” International Conference on Signal Processing (ICSP), Beijing, China, vol. 2, pp. 1116-1119, Oct. 2008.
• Z. Liu, J. Gu, L. Shen, Z. Zhang, “Efficient video object segmentation based on Gaussian mixture model and Markov random field”, International Conference on Signal Processing (ICSP), Beijing, China, vol. 2, pp. 1006-1009, Oct. 2008.
• X. Zhang, J. Yang, Z. Liu, and X. Wang, “Segmenting foreground from similarly colored background,” Optical Engineering, vol. 47, no. 7, pp. 077002, Jul. 2008.
• L. Shen, Z. Liu, Z. Zhang, L. Wang, and G. Wang, “Novel intermode decision algorithm in H.264/AVC,” Journal of Electronic Imaging, vol. 17, no. 1, pp. 013006, Mar. 2008.


2007

• L. Shen, Z. Liu, Z. Zhang, and G. Wang, “An adaptive and fast multiframe selection algorithm for H.264 video coding,” IEEE Signal Processing Letters, vol. 14, no. 11, pp. 836-839, Nov. 2007.
• L. Shen, Z. Zhang, Z. Liu, and W. Zhang, “An adaptive and fast fractional pixel search algorithm in H.264,” Signal Processing, vol. 87, no. 11, pp. 2629-2639, Nov. 2007.
• Z. Liu, L. Shen, Z. Han, and Z. Zhang, “A novel video object tracking approach based on kernel density estimation and Markov random field,” International Conference on Image Processing (ICIP), San Antonio, USA, vol. 3, pp. 373-376, Sept. 2007.
• L. Shen, Z. Liu, Z. Zhang, and X. Shi, “An adaptive and fast H.264 multi-frame selection algorithm based on information from previous searches”, International Conference on Multimedia Expo (ICME), pp. 1591-1594, Beijing, Jul. 2007.
• L. Shen, Z. Liu, Z. Zhang, and X. Shi, “Rate control based on incremental Proportional-Integral-Differential algorithm,” Optical Engineering, vol. 46, no. 7, pp. 077002, Jul. 2007.
• Z. Liu, Y. Lu, and Z. Zhang, “Real-time spatiotemporal segmentation of video objects in the H.264 compressed domain,” Journal of Visual Communication and Image Representation, vol. 18, no. 3, pp. 275-290, Jun. 2007.
• Z. Liu, Z. Zhang, and L. Shen, “Moving object segmentation in the H.264 compressed domain,” Optical Engineering, vol. 46, no. 1, pp. 017003, Jan. 2007.


2006

• Z. Liu, L. Shen, and Z. Zhang, “Automatic video object segmentation from MPEG compressed domain”, International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), Incheon, Korea, pp. 333-336, Apr. 2006.

2005

• N. Peng, J. Yang, and Z. Liu, “Performance analysis for tracking of variable scale objects using mean-shift algorithm,” Optical Engineering, vol. 44, no. 7, pp. 070505, Jul. 2005.
• Z. Liu, J. Yang, and N. Peng, “An efficient face segmentation algorithm based on binary partition tree,” Signal Processing: Image Communication, vol. 20, no. 4, pp. 295-314, Apr. 2005.
• Z. Liu, J. Yang, and N. Peng, “Semi-automatic video object segmentation using seeded region merging and bidirectional projection,” Pattern Recognition Letters, vol. 26, no. 5, pp. 653-662, Apr. 2005.
• N. Peng, J. Yang, and Z. Liu, “Mean shift blob tracking with kernel histogram filtering and hypothesis testing,” Pattern Recognition Letters, vol. 26, no. 5, pp. 605-614, Apr. 2005.


2004

• Z. Liu, and J. Yang, “Interactive video object segmentation: Fast seeded region merging approach,” Electronics Letters, vol. 40, no. 5, pp. 302-304, Mar. 2004.


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