– Implementation of a unified approach for face detection, pose estimation, and landmark localization (CVPR 2012).

Attributes and Semantic Features

•  – Modified implementation of RankSVM to train Relative Attributes (ICCV 2011).

•  – Implementation of object bank semantic features (NIPS 2010). See also 

•  – Software for extracting high-level image descriptors (ECCV 2010, NIPS 2011, CVPR 2012).

  
  

Large-Scale Learning

•  – Source code for fast additive kernel SVM classifiers (PAMI 2013).

•  – Library for large-scale linear SVM classification.

•  – Implementation for Pegasos SVM and Homogeneous Kernel map.

  
  

Fast Indexing and Image Retrieval

• FLANN – Library for performing fast approximate nearest neighbor.

•  – Source code for Kernelized Locality-Sensitive Hashing (ICCV 2009).

•  – Code for generation of small binary codes using Iterative Quantization and other baselines such as Locality-Sensitive-Hashing (CVPR 2011).

•  – Efficient code for state-of-the-art large-scale image retrieval (CVPR 2011).

  
  

Object Detection

•  – Very fast and accurate pedestrian detector (CVPR 2012).

•  – Excellent resource for pedestrian detection, with various links for state-of-the-art implementations.

•  – Enhanced implementation of Viola&Jones real-time object detector, with trained models for face detection.

•  – Source code for branch-and-bound optimization for efficient object localization (CVPR 2008).

  
  

3D Recognition

•  – Library for 3D image and point cloud processing.

  
  

Action Recognition

•  – Source code for action recognition based on the ActionBank representation (CVPR 2012).

•  – software for computing space-time interest point descriptors

•  - C++ code for activity recognition using the velocity histories of tracked keypoints (ICCV 2009)

  
  

Datasets

Attributes

•  – 30,475 images of 50 animals classes with 6 pre-extracted feature representations for each image.

•  – Attribute annotations for images collected from Yahoo and Pascal VOC 2008.

•  – 15,000 faces annotated with 10 attributes and fiducial points.

•  – 58,797 face images of 200 people with 73 attribute classifier outputs.

•  – 13,233 face images of 5,749 people with 73 attribute classifier outputs.

•  – 8,000 people with annotated attributes. Check also this link for another dataset of human attributes.

•  – Large-scale scene attribute database with a taxonomy of 102 attributes.

•  – Variety of attribute labels for the ImageNet dataset.

•  – Data for OSR and a subset of PubFig datasets. Check also this link for the WhittleSearch data.

•  – Images of shopping categories associated with textual descriptions.

  
  

Fine-grained Visual Categorization

•  – Hundreds of bird categories with annotated parts and attributes.

•  – 20,000 images of 120 breeds of dogs from around the world.

•  – 37 category pet dataset with roughly 200 images for each class. Pixel level trimap segmentation is included.

•  – 832 images of 10 species of butterflies.

Face Detection

•  – UMass face detection dataset and benchmark (5,000+ faces)

•  – Classical face detection dataset.

  
  

Face Recognition

•  – Large collection of face recognition datasets.

•  – UMass unconstrained face recognition dataset (13,000+ face images).

•  – includes face recognition grand challenge (FRGC), vendor tests (FRVT) and others.

•  – contains more than 750,000 images of 337 people, with 15 different views and 19 lighting conditions.

• FERET – Classical face recognition dataset.

•  – Easy to use if you want play with simple face datasets including Yale, ORL, PIE, and Extended Yale B.

•  – 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

•  – 10 hours of video taken from a vehicle,350K bounding boxes for about 2.3K unique pedestrians.

•  – Currently one of the most popular pedestrian detection datasets.

•  – Urban dataset captured from a stereo rig mounted on a stroller.

•  – Dataset with image pairs recorded in an crowded urban setting with an onboard camera.

•  – One of 20 categories in PASCAL VOC detection challenges.

•  – Small dataset captured from surveillance cameras.

  
  

Generic Object Recognition

•  – Currently the largest visual recognition dataset in terms of number of categories and images.

•  – 80 million 32x32 low resolution images.

•  – One of the most influential visual recognition datasets.

•  /  – Popular image datasets containing 101 and 256 object categories, respectively.

•  – Online annotation tool for building computer vision databases.

  
  

Scene Recognition

•  – MIT scene understanding dataset.

Feature Detection and Description

•  – Widely used dataset for measuring performance of feature detection and description. Checkfor an evaluation framework.

  
  

Action Recognition

•  – CVPR 2012 tutorial covering various datasets for action recognition.

  
  

RGBD Recognition

•  – Dataset containing 300 common household objects

  
  

Reference:

• MATLAB Normalized Cuts Segmentation Code：