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

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