非原创，原文转自：http://www.yuanyong.org/blog/cv/cv-code-two
Feature Detection and Description
General Libraries:
- VLFeat – Implementation of various feature descriptors (including SIFT, HOG, and LBP) and covariant feature detectors
(including DoG, Hessian, Harris Laplace, Hessian Laplace, Multiscale Hessian, Multiscale Harris). Easy-to-use Matlab interface. See Modern
features: Software – Slides providing a demonstration of VLFeat and also links to other software. Check also VLFeat hands-on session training - OpenCV – Various implementations of modern feature detectors and descriptors (SIFT, SURF, FAST, BRIEF, ORB, FREAK, etc.)
Fast Keypoint Detectors for Real-time Applications:
- FAST – High-speed corner detector implementation for a wide variety of platforms
- AGAST – Even faster than the FAST corner detector. A multi-scale version of this method is used for
the BRISK descriptor (ECCV 2010).
Binary Descriptors for Real-Time Applications:
- BRIEF – C++ code for a fast and accurate interest point descriptor (not invariant to rotations and scale)
(ECCV 2010) - ORB – OpenCV implementation of the Oriented-Brief (ORB)
descriptor (invariant to rotations, but not scale) - BRISK – Efficient Binary descriptor invariant to rotations and scale. It includes a
Matlab mex interface. (ICCV 2011) - FREAK – Faster than BRISK (invariant to rotations and scale) (CVPR 2012)
SIFT and SURF Implementations:
- SIFT: VLFeat, OpenCV, Original
code by David Lowe, GPU implementation, OpenSIFT - SURF: Herbert Bay’s code, OpenCV, GPU-SURF
Other Local Feature Detectors and Descriptors:
- VGG Affine Covariant features – Oxford code for various affine covariant feature detectors
and descriptors. - LIOP descriptor – Source code for the Local Intensity order Pattern (LIOP)
descriptor (ICCV 2011). - Local Symmetry Features – Source code for matching of local symmetry features under large variations
in lighting, age, and rendering style (CVPR 2012).
Global Image Descriptors:
- GIST – Matlab code for the GIST descriptor
- CENTRIST – Global visual descriptor for scene categorization and object detection (PAMI 2011)
Feature Coding and Pooling
- VGG Feature Encoding Toolkit – Source code for various state-of-the-art feature
encoding methods – including Standard hard encoding, Kernel codebook encoding, Locality-constrained linear encoding, and Fisher kernel encoding. - Spatial Pyramid Matching – Source code for feature pooling based on spatial pyramid matching (widely
used for image classification)
Convolutional Nets and Deep Learning
- EBLearn – C++ Library for Energy-Based Learning. It includes several demos and step-by-step instructions to
train classifiers based on convolutional neural networks. - Torch7 – Provides a matlab-like environment for state-of-the-art machine learning algorithms, including a fast implementation
of convolutional neural networks. - Deep Learning - Various links for deep learning software.
Part-Based Models
- Deformable Part-based Detector – Library provided by the authors of the original paper (state-of-the-art
in PASCAL VOC detection task) - Efficient Deformable Part-Based Detector – Branch-and-Bound implementation for a deformable
part-based detector. - Accelerated Deformable Part Model – Efficient implementation of a method that achieves the exact
same performance of deformable part-based detectors but with significant acceleration (ECCV 2012). - Coarse-to-Fine Deformable Part Model – Fast approach for deformable object detection (CVPR 2011).
- Poselets – C++ and Matlab versions for object detection based on poselets.
- Part-based Face Detector and Pose Estimation – Implementation of a unified approach for face detection,
pose estimation, and landmark localization (CVPR 2012).
Attributes and Semantic Features
- Relative Attributes – Modified implementation of RankSVM to train Relative Attributes
(ICCV 2011). - Object Bank – Implementation of object bank semantic features (NIPS 2010). See also ActionBank
- Classemes, Picodes, and Meta-class features – Software for extracting
high-level image descriptors (ECCV 2010, NIPS 2011, CVPR 2012).
Large-Scale Learning
- Additive Kernels – Source code for fast additive kernel SVM classifiers (PAMI 2013).
- LIBLINEAR – Library for large-scale linear SVM classification.
- VLFeat – Implementation for Pegasos SVM and Homogeneous Kernel map.
Fast Indexing and Image Retrieval
- FLANN – Library for performing fast approximate nearest neighbor.
- Kernelized LSH – Source code for Kernelized Locality-Sensitive Hashing (ICCV 2009).
- ITQ Binary codes – Code for generation of small binary codes using Iterative Quantization and other
baselines such as Locality-Sensitive-Hashing (CVPR 2011). - INRIA Image Retrieval – Efficient code for state-of-the-art large-scale image retrieval (CVPR 2011).
Object Detection
- See Part-based Models and Convolutional
Nets above. - Pedestrian Detection at 100fps – Very fast and accurate pedestrian detector (CVPR 2012).
- Caltech Pedestrian Detection Benchmark – Excellent resource for pedestrian
detection, with various links for state-of-the-art implementations. - OpenCV – Enhanced implementation
of Viola&Jones real-time object detector, with trained models for face detection. - Efficient Subwindow Search – Source code for branch-and-bound optimization for efficient
object localization (CVPR 2008).
3D Recognition
- Point-Cloud Library – Library for 3D image and point cloud processing.
Action Recognition
- ActionBank – Source code for action recognition based on the ActionBank representation
(CVPR 2012). - STIP Features – software for computing space-time interest point descriptors
- Independent Subspace Analysis – Look for Stacked ISA for Videos (CVPR 2011)
- Velocity Histories of Tracked Keypoints - C++ code for activity recognition using the velocity
histories of tracked keypoints (ICCV 2009)
Datasets
Attributes
- Animals with Attributes – 30,475 images of 50 animals classes with 6 pre-extracted feature representations
for each image. - aYahoo and aPascal – Attribute annotations for images collected from Yahoo and Pascal VOC 2008.
- FaceTracer – 15,000 faces annotated with 10 attributes and fiducial points.
- PubFig – 58,797 face images of 200 people with 73 attribute classifier outputs.
- LFW – 13,233 face images of 5,749 people with 73 attribute classifier outputs.
- Human Attributes – 8,000 people with annotated attributes. Check also this link for
another dataset of human attributes. - SUN Attribute Database – Large-scale scene attribute database with a taxonomy of 102 attributes.
- ImageNet Attributes – Variety of attribute labels for the ImageNet dataset.
- Relative attributes – Data for OSR and a subset of PubFig datasets. Check also this link for
the WhittleSearch data. - Attribute Discovery Dataset – Images of shopping categories associated with textual descriptions.
Fine-grained Visual Categorization
- Caltech-UCSD Birds Dataset – Hundreds of bird categories with annotated parts and
attributes. - Stanford Dogs Dataset – 20,000 images of 120 breeds of dogs from around the world.
- Oxford-IIIT Pet Dataset – 37 category pet dataset with roughly 200 images for each class. Pixel
level trimap segmentation is included. - Leeds Butterfly Dataset – 832 images of 10 species of butterflies.
- Oxford Flower Dataset – Hundreds of flower categories.
Face Detection
- FDDB – UMass face detection dataset and benchmark (5,000+ faces)
- CMU/MIT – Classical face detection dataset.
Face Recognition
- Face Recognition Homepage – Large collection of face recognition datasets.
- LFW – UMass unconstrained face recognition dataset (13,000+ face images).
- NIST Face Homepage – includes face recognition grand challenge (FRGC), vendor tests (FRVT) and others.
- CMU Multi-PIE – contains more than 750,000 images of 337 people, with 15 different views and 19 lighting conditions.
- FERET – Classical face recognition dataset.
- Deng Cai’s face dataset in Matlab Format – Easy to use if you want play with simple
face datasets including Yale, ORL, PIE, and Extended Yale B. - SCFace – Low-resolution face dataset captured from surveillance cameras.
Handwritten Digits
- MNIST – large dataset containing a training set of 60,000 examples, and a test set of 10,000 examples.
Pedestrian Detection
- Caltech Pedestrian Detection Benchmark – 10 hours of video taken from a vehicle,350K
bounding boxes for about 2.3K unique pedestrians. - INRIA Person Dataset – Currently one of the most popular pedestrian detection datasets.
- ETH Pedestrian Dataset – Urban dataset captured from a stereo rig mounted on a stroller.
- TUD-Brussels Pedestrian Dataset – Dataset with image pairs recorded in an crowded urban setting
with an onboard camera. - PASCAL Human Detection – One of 20 categories in PASCAL VOC detection challenges.
- USC Pedestrian Dataset – Small dataset captured from surveillance
cameras.
Generic Object Recognition
- ImageNet – Currently the largest visual recognition dataset in terms of number of categories and images.
- Tiny Images – 80 million 32x32 low resolution images.
- Pascal VOC – One of the most influential visual recognition datasets.
- Caltech 101 / Caltech
256 – Popular image datasets containing 101 and 256 object categories, respectively. - MIT LabelMe – Online annotation tool for building computer vision databases.
Scene Recognition
- MIT SUN Dataset – MIT scene understanding dataset.
- UIUC Fifteen Scene Categories – Dataset of 15 natural scene categories.
Feature Detection and Description
- VGG Affine Dataset – Widely used dataset for measuring performance of feature detection
and description. CheckVLBenchmarks for an evaluation framework.
Action Recognition
- Benchmarking Activity Recognition – CVPR 2012 tutorial
covering various datasets for action recognition.
RGBD Recognition
- RGB-D Object Dataset – Dataset containing 300 common household objects
Reference:
[1]: http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html