Travelled to:1 × Japan
1 × Sweden
1 × USA
2 × Canada
2 × China
3 × United Kingdom
Collaborated with:H.Lu S.Ma Z.Li J.Cheng R.Huang C.Li J.Wang J.Liu D.N.Metaxas W.Yan Y.Chen X.Tang C.Zhao W.Fu J.Wang X.Bai L.Yang F.Meng W.Dong Y.Wang J.Xu
Talks about:recognit (4) analysi (4) kernel (4) featur (4) base (4) discrimin (3) activ (3) face (3) regress (2) geometr (2)
Person: Qingshan Liu
DBLP: Liu:Qingshan
Contributed to:
2014201220082006200620042004200420022002Wrote 13 papers:
- ICPR-2014-LiYLMDWX #higher-order #multi
- Multiple-Output Regression with High-Order Structure Information (CL, LY, QL, FM, WD, YW, JX), pp. 3868–3873.
- ICPR-2014-ZhaoFW0LL #modelling #process #recognition
- Discriminative Context Models for Collective Activity Recognition (CZ, WF, JW, XB, QL, HL), pp. 648–653.
- ICPR-2012-LiLLL #distance #estimation #learning #metric
- Learning distance metric regression for facial age estimation (CL, QL, JL, HL), pp. 2327–2330.
- ICPR-2008-LiLCL #approach #image #segmentation
- A variational inference based approach for image segmentation (ZL, QL, JC, HL), pp. 1–4.
- ICPR-2008-LiLLM #geometry
- Lennard-Jones force field for Geometric Active Contour (ZL, QL, HL, DNM), pp. 1–4.
- ICPR-v2-2006-LiLL #framework #geometry #multi #using
- A Geometric Active Contour Framework using Multi-Cue and Local Feature (ZL, QL, HL), pp. 113–116.
- ICPR-v3-2006-LiuYLM #recognition #robust #similarity
- Occlusion Robust Face Recognition with Dynamic Similarity Features (QL, WY, HL, SM), pp. 544–547.
- ICPR-v1-2004-ChengLLC #classification #component #independence #kernel #using
- Texture Classification Using Kernel Independent Component Analysi (JC, QL, HL, YWC), pp. 620–623.
- ICPR-v2-2004-LiuTLM #analysis #kernel #recognition
- Kernel Scatter-Difference Based Discriminant Analysis For Face Recognition (QL, XT, HL, SM), pp. 419–422.
- ICPR-v3-2004-LiuCLM #distance #image #kernel #re-engineering
- Distance Based Kernel PCA Image Reconstruction (QL, JC, HL, SM), pp. 670–673.
- ICPR-v3-2004-WangLL #analysis #towards
- Tensor Voting Toward Feature Space Analysis (JW, HL, QL), pp. 462–465.
- ICPR-v2-2002-LiuHLM #analysis #kernel #recognition
- Kernel-Based Optimized Feature Vectors Selection and Discriminant Analysis for Face Recognition (QL, RH, HL, SM), pp. 362–365.
- ICPR-v3-2002-HuangLLM #problem
- Solving the Small Sample Size Problem of LDA (RH, QL, HL, SM), pp. 29–32.