2013计算机视觉代码合集三

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

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

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 32×32 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

Feature Detection and Description

Action Recognition

Benchmarking Activity Recognition

– CVPR 2012 tutorial covering various datasets for action recognition.

RGBD Recognition

Reference: