图像识别领域的一些code

图像识别领域的一些code

转自：http://blog.163.com/pz124578@126/blog/static/23522694201343110495537/

ps：里面的一些方法都是目前最新的。每个领域当然可以做大量扩充，根据需要嘛。

Non-exhaustive list of state-of-the-art implementations related to visual recognition and search. There is no warranty for the source code links below – use them at your own risk!

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. SeeModern 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)

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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. CheckVLBenchmarksfor 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

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Related Courses

• Visual Recognition - Kristen Grauman, U. Texas, Fall 2012.
• The Cutting Edge of Computer Vision - Fei-Fei Li, Stanford, Spring 2011.
• Learning-based Methods in Vision - Alyosha Efros and Leonid Sigal, CMU, Spring 2012.
• Grounding Object Recognition and Scene Understanding - Antonio Torralba, MIT, Fall 2011.