已掌握的方法

图像处理:
 全局特征
 局部特征
 图像质量评价
 显著性检测
 图像滤波

IP: Image Process
Global Feature
Local Feature
Image Quality Analysis
Salience Detection 
Image Filtering

Year

Topic

Method

Reference (Formal)

2009

Global Feature

PHOG: Pyramids of Histograms of Oriented Gradients

A. Bosch, A. Zisserman, and X. Munoz, Representing shape with a spatial pyramid kernel, CIVR, 2007

2009

Global Feature

Gist

A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope, IJCV, 2001

2009

Local Feature

SIFT: Scale Invariant Feature Transform

D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.

2010

Local Feature

Affine-SIFT: Affine-Scale Invariant Feature Transform

J.M. Morel and G.Yu, ASIFT, A new framework for fully affine invariant image comparison. SIAMJournal on Imaging Sciences, 2009

2011

Local Feature

LBP: Local Binary Pattern

M. Pietikainen and J. Heikkila, CVPR 2011 Tutorial

2012

Local Feature

PCA-SIFT: Principal Component Analysis - Scale Invariant Feature Transform

Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004

2012

Local Feature

SC: Shape Context

S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contexts, PAMI, 2002

2012

Image Quality Analysis

SSim: Structure Similarity

Image quality assessment: from error visibility to structural similarity [J]. IEEE Trans. Image Process, 2004, 13(4): 600–612.

2012

Image Quality Analysis

IW-SSim: Information Content Weighted Structure Similarity

Z. Wang and Q. Li, Information content weighting for perceptual image quality assessment, IEEE Transactions on Image Processing, vol. 20, no 5, pp. 1185-1198, May 2011.

2012

Image Quality Analysis

MS-SSim: Multi-scale Structure Similarity

Wang Z, Simoncelli E P, Bovik A C. Multi-scale  structural similarity for image quality assessment [J].  Proc. IEEE Asilomar Conf. Signals, Syst. Comput., 2003:. 1398–1402.

2012

Image Quality Analysis

MSEMean Square Error

Wang Z,  Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity [J]. IEEE Trans. Image Process, 2004, 13(4): 600–612.

2012

Image Quality Analysis

VSNR: Visual Signal-to-Noise Ratio

Chandler D M, Hemami S S. VSNR: a Wavelet based visual signal-to-noise ratio for  natural images [J]. IEEE Trans. Image Process, 2007,16(9): 2284–2298.

2012

Image Quality Analysis

3-SSIM: 3 -Chanle Structure Similarity

Li C and  Bovik A C. Three-component weighted structural similarity index[C]\\ Proceedings of the International Society for Optical Engineering, 2009.

2012

Image Resizing

Context-Aware:Context

ShaiAvidan, Ariel Shamir. Seam carving for content-aware image resizing. ACM SIGGRAPH '07. 26(3). 2007

2012

Salience Detection

Itti Model

Itti, L. A model of saliency-based visual attention for rapid scene analysis . Pattern Analysis and Machine Intelligence, IEEE Transactions on. 20(11): 1254 - 1259. 1998.

2012

Salience Detection

MSSS: Saliency Detection using Maximum Symmetric

Achanta, R.; Süsstrunk, S. Saliency detection using maximum symmetric surround. Image Processing (ICIP), 2010 17th IEEE International Conference on. 2653 - 2656, 2010.

2012

Salience Detection

AIM: Attention based on Information Maximization

Bruce, N.D.B., Tsotsos, J.K., Saliency Based on Information Maximization. Advances in Neural Information Processing Systems, 18, pp. 155-162, June 2006. Selected for oral presentation

2012

Salience Detection

SF: Saliency Filters: Contrast Based Filtering for Salient Region Detection

Perazzi, F. Krahenbuhl, P. ;Pritch, Y. ; Hornung, A. Saliency Filters: Contrast Based Filtering for Salient Region Detection. Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. 733 - 740. 2012

2012

  

Salience Detection

SR: Sspectral Residual

XiaodiHouLiqing Zhang. Saliency Detection: A Spectral Residual Approach. Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on. 1-8. 2007.

2012

Salience Detection

HC: Histogram-based Contrast,  RC: Region-based Contrast

M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region Detection. CVPR, 2011

2012

Salience Detection

CRF: Conditional Random Field

Tie Liu; Jian Sun; Nan-NingZhengXiaoou Tang; Heung-Yeung Shum. Learning to Detect A Salient Object. Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on. 1-8. 2007.

2012

Salience Detection

IG: Interest Gaussian

R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009

2012

Salience Detection

Context-Aware:Context

S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. In CVPR, 2010.

2012

Salience Detection

Salient region detection and segmentation.

R. Achanta, F. Estrada, P. Wils, and S. S¨usstrunk. Salient region detection and segmentation. In ICVS, pages 66–75. Springer, 2008. 410, 412, 414

2012

Salience Detection

GBVSGraph-Based Visual Saliency

J. Harel, C. Koch, and P. Perona. Graph-based visua saliency. In NIPS, pages 545–552, 2006. 410, 412, 414

2012

Salience Detection

SUNSaliency Using Natural statistics

A Bayesian Framework for Saliency Using Natural Statistics

2012

Salience Detection

Fuzzy Growing

Y.-F. Ma and H.-J. Zhang, “Contrast-based image attention analysis by using fuzzy growing,” ACM International Conference on Multimedia, pp. 374–381, November 2003.

2012

Salience Detection

DSDDiscriminant Saliency Detector

Achanta, R. Discriminant Saliency for Visual Recognition from Cluttered Scenes[C]/Proc. Of IEEE  Conference Publications. On Hong Kong IEEE press. 2010,Pages: 2653 - 2656

2012

Salience Detection

HSHuman Saliency

 Judd, T. Ehinger, K. Learning to Predict Where Humans Look[C]/Proc. Of IEEE  Conference Publications. On Kyoto,Pages:2106 - 2113

2009

Image Filtering

BF: Bilateral Filtering

S. Paris and F. Durand, A Fast Approximation of the Bilateral Filter using a Signal Processing Approach, ECCV, 2006

2012

Image Filtering

BF: Bilateral Filtering

Q. Yang, K.-H. Tan and N. Ahuja,  Real-time O(1) Bilateral Filtering,  CVPR 2009

2012

Image Filtering

BF: Bilateral Filtering

Q. Yang, S. Wang and N. Ahuja , Real-time Specular Highlight Removal Using Bilateral Filtering, ECCV 2010

2009

Image Filtering

BF: Bilateral Filtering

S. Paris and F. Durand, A Fast Approximation of the Bilateral Filter using a Signal Processing Approach, ECCV, 2006










 机器学习

 判决模型
 生成模型
 图模型
 聚类
 流形
 核方法
 距离函数
 迁移学习
 集成学习


ML: Machine Learning

Discriminative Model
Generated Model
Graph Model
Clustering
Manifold
Kernel
Distance
Transfer Learning
Ensemble Learning

2008

Discriminative Model

SVM: Support Vector Machines

C.-W. Hsu, C.-J. Lin. A simple decomposition method for support vector machines , Machine Learning 46(2002), 291-314

2010

Discriminative Model

LDA: Linear Discriminant Analysis

C. Strecha, A. M. Bronstein, M. M. Bronstein and P. Fua. LDAHash: Improved matching with smaller descriptors, PAMI, 2011.

2012

Discriminative Model

Netlab: Networks Laboratory

C. M. Bishop, Neural Networks for PatternRecognitionOxfordUniversityPress, 1995

2009

Generated Model

PLSA: Probabilistic Latent Semantic Analysis

Fei-Fei, L. and Perona, P., A Bayesian Heirarcical Model for Learning Natural Scene Categories, Proc. CVPR, 2005.

2010

Generated Model

LDA: Latent Dirichlet Allocation

Tracking E. B. Graphical Models for Visual Object Recognition and  Sudderth Doctoral Thesis, Massachusetts Institute of Technology, May 2006.

2010

Generated Model

HDP: Hierarchical Dirichlet Processes

Targets E. Fox, E. Sudderth, and A. Willsky. Hierarchical DirichletProcesses for Tracking Maneuvering International Conference on Information Fusion, July 2007.

2010

Generated Model

TDP: Transformed Dirichlet Processes

Processes E. Sudderth, A. Torralba, W. Freeman, and A. Willsky. Describing Visual Scenes using Transformed Dirichlet. Neural Information Processing Systems, Dec. 2005.

2009

Graph Model

CRF: Conditional Random Field, MRF: Markov Random Field

S. V. N. VishwanathanNicol N. Schraudolph. Mark W. Schmidt. Kevin P. Murphy. Accelerated training of conditional random fields with stochastic gradient methods. Proceeding ICML '06 Proceedings of the 23rd international conference on Machine learning. Pages 969 - 976. 2006.

2009

Graph Model

ICM: Iterated Conditional Modes

S Li. Markov Random Field Modeling in Computer Vision Springer-Verlag, 1995

2010

Clustering

AP: Affinity Propagation (k-centers; k-means; klogkmdgEM: Mixture Directional Gaussian - Exception Maximum; migEM: Mixture Isotropic Gaussian - Exception Maximum; Clusteing with Quantized/ Quantized Extension)

Clustering by Passing Messages Between Data Points. Brendan J. Frey and Delbert Dueck, Science 315, 972–976, February 2007.

2010

Manifold

PCA: Principal Component Analysis, LE: LaplacianEigenmap, LLE: Local Linear Embedding, HLLE: Hessian Local Linear Embedding, Isomap: Isometric Feature Mapping

L.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik. Dimensionality Reduction: A Comparative Review. TilburgUniversityTechnical Report, TiCC-TR 2009-005, 2009.

2012

Kernel

SKMsmo: Support Kernel Machine - Sequential Minimal Optimization

Bach, F.R. Lanckriet, G.R.G., Jordan, M.I. Fast Kernel Learning using Sequential Minimal Optimization . Technical Report CSD-04-1307, Division of Computer Science, University of California , Berkeley. 2004

2012

Kernel

SimpleMKL: Simple Multi-Kernel Learning

A. Rakotomamonjy, F. Bach, S. Canu, and Y. GrandvaletSimplemkl. JMRL, 2008

2012

Distance

EMD: Earth Mover's Distance

H. Ling and K. Okada, An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison, PAMI 2007

2012

Distance

Pwmetric: Pair-Wise Metric

Modeling and Estimating Persistent Motion with Geometric Flows. DahuaLin, Eric Grimson, and John Fisher. 23rd IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.

2009

Ensemble Learning

Boosting

A. Vezhnevets, O. Barinova . Avoiding Boosting Overfitting by Removing 'Confusing Samples. ECML 2007, Oral.

2009

Ensemble Learning

Boosting

Theoretical and Empirical Analysis of Diversity in Non-Stationary Learning, R. Stapenhurst and G. Brown, 2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments. 2011.

2009

Ensemble Learning

Alignment

Z. H. Zhou, W. Tang. Clusterer Ensemble [J]. Knowledge-Based Systems, 2006, 19(1): 77-83

2012

Transfer Learning

CCTL: Cross Category Transfer Learning

Guo-Jun QiCharuAggarwal, Yong RuiQiTianShiyu Chang and Thomas Huang. Towards Cross-Category Knowledge Propagation for Learning Visual Concepts, in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011), Colorado Springs, Colorado, June 21-23, 2011.

2012

Transfer Learning

MSTR: Multi-Source Transfer Learning

Ping LuoFuzhenZhuangHuiXiongYuhongXiong, Qing He. Transfer learning from multiple source domains via consensus regularization. Proceeding CIKM '08 Proceedings of the 17th ACM conference on Information and knowledge management. Pages 103-112. 2008.





 计算机视觉: 

 图像超分辨率重建
 图像配准
 图像分割
 图像抠图
 图像修补
 图像分类
 图像检索
 图像理解
 光流
 目标跟踪
 图像深度估计
 语义分析
 数据集


CV: Computer Vision
Image Super-Resolution
Image Registration
Image Segmentation
Image Matting
Image Inpainting
Image Classification
Image Retrieval
Image Understanding
Optical Flow
Object Tracking
Image Depth
Semantic Analysis
Data Set

2012

Image Super-Resolution

Super-resolution as Sparse Representation

Jianchao Yang, John Wright, Thomas Huang, and Yi Ma. Image Super-resolution as Sparse Representation of Raw Image Patches. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.

2009

Image Registration

Base on SIFT(Scale Invariant Feature Transform)

D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.

2011

Image Segmentation

SP: Super Pixcels

X. Ren and J. Malik. Learning a classification model for segmentation. ICCV, 2003

2012

Image Segmentation

GC: Graph Cut (Max Flow/ Min Cut)

L. Gorelick, A. Delong, O. Veksler, Y. Boykov, Recursive MDL via Graph Cuts: Application to Segmentation, International Conference on Computer Vision. 2011,

2012

Image Segmentation

NcutNormal Cut

J. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000

2012

Image Matting

Closed-Form Solution

AnatLevin,DaniLischinski,andYairWeiss.A Closed-Form Solution to Natural Imae Matting,2006

2012

Image Matting

SpectralMatting

AnatLevin,AlexRav-Acha,andDaniLischinski. Spectral Matting,2008

2012

Image Matting

KnockOut

A. Berman, A. Dadourian, and P. Vlahos. Method for removing from an image the background surrounding a selected object,2000

2012

Image Matting

BayesianMatting

Yung-Yu Chuang,Brian Curless1David H. Salesin1, Richard Szeliski.A Bayesian Approach to Digital Matting,2000

2012

Image Matting

Learning Based Matting

YuanjieZheng,ChandraKambhamettu.Learning Based Digital Matting,2009

2012

Image Inpainting

CriminisiInpainting

Antonio Criminisi, Patrick Perez, and KentaroToyama.Object Removal by Exemplar-Based Inpainting,2003

2012

image Classification

SC: Sparse Coding

Sparse Coding for Image Classification

2010

image Classification

ICA: Independent Component Analysis

Hyvärinen A. Testing the ICAmixing matrix based on inter-subject or inter-session consistency. NeuroImage.

2010

image Classification

FastICA: Fast Independent Component Analysis

A. Hyvärinen, J. Karhunen, E. Oja . Independent Component Analysis. Wiley-Interscience. 2001

2010

Image Classification

SPM: Spatial Pyramid Matching, BoF: Bag of Feature (BoW: Bag of Word)

S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New York, June 2006, vol. II, pp. 2169-2178.

2011

Image Classification

LLC: Locality-constrained Linear Coding

Jianchao Yang, Kai Yu, Yihong Gong, and Thomas Huang. Linear spatial pyramid matching using sparse coding for image classification. CVPR'09.

2011

Image Classification

EMK: Efficient Match Kernels

Liefeng Bo, CristianSminchisescu Efficient Match Kernels between Sets of Features for Visual Recognition, Advances in Neural Information Processing Systems (NIPS), December, 2009.

2008

Image Retrieval

The Pyramid Match: Efficient Matching for Retrieval and Recognition

K. Grauman and T. Darrell.  The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features, ICCV 2005

2012

Image Understanding

TSU: Towards Total Scene Understanding

Li-Jia Li, Richard Socher and Li Fei-Fei. Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework. Computer Vision and Pattern Recognition (CVPR) 2009.

2012

Image Understanding

Object Context

Yong Jae Lee and Kristen Grauman. Object-Graphs for Context-Aware Category Discovery. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco , CA, June 2010.

2012

Optical Flow

Black and Anandan's Optical Flow

Black, M.J. Anandan, P. A framework for the robust estimation of optical flow. Computer Vision, 1993. Proceedings. Fourth International Conference on. 1993.

2012

Object Tracking

PF: Particle Filter (LASSO: Least Absolute Shrinkage and Selection Operator)

X. Mei and H. Ling. Robust Visual Tracking and Vehicle Classification via Sparse Representation. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 33(11):2259--2272, 2011.

2012

Object Tracking

Incremental Learning

D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual Tracking, IJCV 2007

2012

Object Tracking

On-Line Boosting

Tracking the Invisible: Learning Where the Object Might be H. Grabner, J. Matas, L. Van Gool, and P. Cattin In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010

2012

Object Tracking

Motion Tracking

C. Stauffer and W. E. L. Grimson. Learning patterns of activity using real-time tracking, PAMI, 2000

2012

Object Tracking

Kanade-Lucas-Tomasi Feature Tracker

B. D. Lucas and T. Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. IJCAI, 1981

2012

Object Tracking

Tracking Decomposition

J Kwon and K. M. Lee, Visual Tracking Decomposition, CVPR 2010

2012

Object Tracking

Adaptive Structural Local Sparse Appearance Model

XuJiaHuchuan Lu, Minghsuan Yang, Visual Tracking via Adaptive Structural Local Sparse Appearance Model, International Conference on Computer Vision and Pattern Recognition,2012,.

2012

Object Tracking

Sparsity-based Collaborative Model

Wei ZhongHuchuan Lu, Minghsuan Yang, Robust Object Tracking via Sparsity-based Collaborative Model, International Conference on Computer Vision and Pattern Recognition,2012.

2012

Image Depth

DC: Dark Channel

Kaiming He, Jian Sun, and Xiaoou Tang, Single Image Haze Removal using Dark Channel Prior, by  in TPAMI 2011.

2010

Semantic Analysis

Wordnet

WordNet 3.0 Reference Manual

2008

Data Set

Caltech 256: Caltech-256 benchmarks

Citation: caltech-256 object Gategory dataset[c].Greg GriffinAlex Holub,California Institute of Technology on 2007

2008

Data Set

VOCdevkit: PASCAL VOC Development Kits (PASCAL: Pattern Analysis, Statistical Modelling and Computational Learning)

Citation: The PASCAL Visual Object Classes Challenge 2011 (VOC2011) Development Kit. Mark Everingham John WinnMarkEveringham John Winn


2009

Data Set

LabelMe

Citation: Modeling the shape of the scene: a holistic representation of the spatial envelope. A. Oliva, A. Torralba. International Journal of Computer Vision, Vol. 42(3): 145-175, 2001.


2009

Data Set

Eight outdoor scene categories

AudeOliva, Antonio Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope. International Journal of Computer Vision, Vol. 42(3): 145-175, 2001.


2009

Data Set

Fifteen Scene Categories

Svetlana LazebnikCordeliaSchmid, and Jean Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2006.


2009

Data Set

SUN Database: Scene UNderstanding Database.

J. Xiao, J. Hays, K. Ehinger, A. Oliva, and A. Torralba. SUN Database: Large scale Scene Recognition from Abbey to Zoo. IEEE Conference on Computer Vision and Pattern Recognition. CVPR. 2010.


2012

Data Set

SegBanch: The Berkely Segmentation Dataset and Benchmark

VOI


2012

Data Set

Saliency Benchmark

R. Subramanian, H. Katti, N. Sebe1, M. Kankanhalli, T-S. Chua, An Eye Fixation Database for Saliency Detection in Images,  European Conference on Computer Vision (ECCV 2010), Heraklion, Greece, September 2010


2012

Data Set

SegBanch: The Berkely Segmentation Dataset and Benchmark

X. Ren, C. Fowlkes, J. Malik. Figure/Ground Assignment in Natural Images, ECCV, Graz , Austria, (May 2006).


2012

Data Set

Flikcer

Citation: Flickrshapetiles : Location data created from WOEidgeotaggedFlickr photos


2012

Data Set

YL face: Yale Face Database

Citation: From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose[J].Georghiades, A.S. and Belhumeur .IEEE Trans. Pattern Anal. Mach. Intelligence on 2001.pages:643-660


2012

Data Set

Saliency Benchmark

R. Subramanian, H. Katti, N. Sebe1, M. Kankanhalli, T-S. Chua, An Eye Fixation Database for Saliency Detection in Images,  European Conference on Computer Vision (ECCV 2010), Heraklion, Greece, September 2010


2012

Data Set

ImageCLEF Plant (CLEF: key/ french)

GoëauHervé; Bonnet, Pierre; Joly, Alexis; BoujemaaNozhaBarthelemy, Daniel; Molino, Jean-François; Birnbaum, Philippe; Mouysset, Elise; Picard, Marie. The CLEF 2011 plant image classification task. CLEF 2011 working notes, Amsterdam, The Netherlands, 2011.


2012

Data Set

ImageCLEFphoto (CLEF: key/ french)

Citation: Diversity in Photo Retrieval: Overview of the ImageCLEFPhoto Task 2009. Monica Lestari Paramita, Mark Sanderson,Lecture Notes in Computer Science, 2010, Volume 6242/2010, 45-59,










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