学步园

转载自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