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)

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

Reference:

[1]: http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html