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 | MSE:Mean 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 | XiaodiHou; Liqing 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-NingZheng; Xiaoou 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 | GBVS:Graph-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 | SUN:Saliency 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 | DSD:Discriminant 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 | HS:Human 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 |
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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
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| 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 PatternRecognitionㄝOxfordUniversityPress, 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. Vishwanathan. Nicol 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; klogk; mdgEM: 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. Grandvalet. Simplemkl. 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 Qi, CharuAggarwal, Yong Rui, QiTian, Shiyu 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 Luo, FuzhenZhuang, HuiXiong, YuhongXiong, 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. |
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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 | Ncut: Normal 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 | XuJia, Huchuan 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 Zhong, Huchuan 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 Griffin,Alex 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 Lazebnik, CordeliaSchmid, 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) | Goëau, Hervé; Bonnet, Pierre; Joly, Alexis; Boujemaa, Nozha; Barthelemy, 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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