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本文属于转载内容，也在笔者另外一个博客（http://blog.csdn.net/qq_37608890/article/details/79352633）发布。
来源：http://blog.csdn.net/flyingpig851334799/article/details/47449847。
一、特征提取Feature Extraction：
- SIFT [1] [][] []
- PCA-SIFT [2] []
- Affine-SIFT [3] []
- SURF [4] [] []
- Affine Covariant Features [5] []
- MSER [6] [] []
- Geometric Blur [7] []
- Local Self-Similarity Descriptor [8] []
- Global and Efficient Self-Similarity [9] []
- Histogram of Oriented Graidents [10] [] []
- GIST [11] []
- Shape Context [12] []
- Color Descriptor [13] []
- Pyramids of Histograms of Oriented Gradients []
- Space-Time Interest Points (STIP) [14][] []
- Boundary Preserving Dense Local Regions [15][]
- Weighted Histogram[]
- Histogram-based Interest Points Detectors[][]
- An OpenCV - C++ implementation of Local Self Similarity Descriptors []
- Fast Sparse Representation with Prototypes[]
- Corner Detection []
- AGAST Corner Detector: faster than FAST and even FAST-ER[]
- Real-time Facial Feature Detection using Conditional Regression Forests[]
- Global and Efficient Self-Similarity for Object Classification and Detection[]
- WαSH: Weighted α-Shapes for Local Feature Detection[]
- HOG[]
- Online Selection of Discriminative Tracking Features[]
二、图像分割Image Segmentation：
- Normalized Cut [1] []
- Gerg Mori’ Superpixel code [2] []
- Efficient Graph-based Image Segmentation [3] [] []
- Mean-Shift Image Segmentation [4] [] []
- OWT-UCM Hierarchical Segmentation [5] []
- Turbepixels [6] [] [] []
- Quick-Shift [7] []
- SLIC Superpixels [8] []
- Segmentation by Minimum Code Length [9] []
- Biased Normalized Cut [10] []
- Segmentation Tree [11-12] []
- Entropy Rate Superpixel Segmentation [13] []
- Fast Approximate Energy Minimization via Graph Cuts[][]
- Efficient Planar Graph Cuts with Applications in Computer Vision[][]
- Isoperimetric Graph Partitioning for Image Segmentation[][]
- Random Walks for Image Segmentation[][]
- Blossom V: A new implementation of a minimum cost perfect matching algorithm[]
- An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Computer Vision[][]
- Geodesic Star Convexity for Interactive Image Segmentation[]
- Contour Detection and Image Segmentation Resources[][]
- Biased Normalized Cuts[]
- Max-flow/min-cut[]
- Chan-Vese Segmentation using Level Set[]
- A Toolbox of Level Set Methods[]
- Re-initialization Free Level Set Evolution via Reaction Diffusion[]
- Improved C-V active contour model[][]
- A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction[][]
- Level Set Method Research by Chunming Li[]
- ClassCut for Unsupervised Class Segmentation[e]
- SEEDS: Superpixels Extracted via Energy-Driven Sampling ][]
三、目标检测Object Detection：
- A simple object detector with boosting []
- INRIA Object Detection and Localization Toolkit [1] []
- Discriminatively Trained Deformable Part Models [2] []
- Cascade Object Detection with Deformable Part Models [3] []
- Poselet [4] []
- Implicit Shape Model [5] []
- Viola and Jones’s Face Detection [6] []
- Bayesian Modelling of Dyanmic Scenes for Object Detection[][]
- Hand detection using multiple proposals[]
- Color Constancy, Intrinsic Images, and Shape Estimation[][]
- Discriminatively trained deformable part models[]
- Gradient Response Maps for Real-Time Detection of Texture-Less Objects: LineMOD []
- Image Processing On Line[]
- Robust Optical Flow Estimation[]
- Where's Waldo: Matching People in Images of Crowds[]
- Scalable Multi-class Object Detection[]
- Class-Specific Hough Forests for Object Detection[]
- Deformed Lattice Detection In Real-World Images[]
- Discriminatively trained deformable part models[]
四、显著性检测Saliency Detection：
- Itti, Koch, and Niebur’ saliency detection [1] []
- Frequency-tuned salient region detection [2] []
- Saliency detection using maximum symmetric surround [3] []
- Attention via Information Maximization [4] []
- Context-aware saliency detection [5] []
- Graph-based visual saliency [6] []
- Saliency detection: A spectral residual approach. [7] []
- Segmenting salient objects from images and videos. [8] []
- Saliency Using Natural statistics. [9] []
- Discriminant Saliency for Visual Recognition from Cluttered Scenes. [10] []
- Learning to Predict Where Humans Look [11] []
- Global Contrast based Salient Region Detection [12] []
- Bayesian Saliency via Low and Mid Level Cues[]
- Top-Down Visual Saliency via Joint CRF and Dictionary Learning[][]
- Saliency Detection: A Spectral Residual Approach[]
五、图像分类、聚类Image Classification, Clustering
- Pyramid Match [1] []
- Spatial Pyramid Matching [2] []
- Locality-constrained Linear Coding [3] [] []
- Sparse Coding [4] [] [
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