常用图像数据集：标注、检索
1.搜狗实验室数据集：
http://www.sogou.com/labs/dl/p.html
互联网图片库来自sogou图片搜索所索引的部分数据。其中收集了包括人物、动物、建筑、机械、风景、运动等类别，总数高达2,836,535张图片。对于每张图片，数据集中给出了图片的原图、缩略图、所在网页以及所在网页中的相关文本。200多G
2
http://www.imageclef.org/
IMAGECLEF致力于位图片相关领域提供一个基准（检索、分类、标注等等） Cross Language Evaluation Forum (CLEF) 。从2003年开始每年举行一次比赛.
http://staff.science.uva.nl/~xirong/index.php?n=Main.Dataset
3
Xiaorong Li 维护的数据集。PhD ,Intelligent Systems Lab Amsterdam.research on video and image retrieval.
- Flickr-3.5M: A collection of 3.5 million social-tagged images.
- Social20: A ground-truth set for tag-based social image retrieval.
- Biconcepts2012test: A ground-truth set for retrieving bi-concepts (concept pairs) in unlabeled images.
- neg4free: A set of negative examples automatically harvested from social-tagged images for 20 PASCAL VOC concepts.
5
http://lms.comp.nus.edu.sg/research/NUS-WIDE.htm
To our knowledge, this is the largest real-world web image dataset comprising over 269,000 images with over 5,000 user-provided tags, and ground-truth of 81 concepts for the entire dataset. The dataset is much larger than the popularly available Corel and Caltech 101 datasets. Though some datasets comprise over 3 million images, they only have ground-truth for a small fraction of images. Our proposed NUS-WIDE dataset has the ground-truth for the entire dataset.
6.
http://www.cs.washington.edu/research/imagedatabase/
7.
http://lear.inrialpes.fr/~jegou/data.php
Jegou的数据集，不过Jegou是专门做CBIR的，图像有ground truth，没有标注。
8.
http://www.robots.ox.ac.uk/~vgg/data/oxbuildings/
vgg的osford building dataset。也是专门CBIR的数据。
9.
http://acmmm13.org/submissions/call-for-multimedia-grand-challenge-solutions/msr-bing-grand-challenge-on-image-retrieval-scientific-track/
The dataset for the Microsoft Image Grand Challenge on Image Retrieval
另外介绍cvpaper上的整理的数据集
Participate in Reproducible Research
Detection
- PASCAL VOC 2009 dataset
- Classification/Detection Competitions, Segmentation Competition, Person Layout Taster Competition datasets
- LabelMe dataset
- LabelMe is a web-based image annotation tool that allows researchers to label images and share the annotations with the rest of the community. If you use the database, we only ask that you contribute to it, from time to time, by using the labeling tool.
- BioID Face Detection Database
- 1521 images with human faces, recorded under natural conditions, i.e. varying illumination and complex background. The eye positions have been set manually.
- CMU/VASC & PIE Face dataset
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- Yale Face dataset
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- Caltech
- Cars, Motorcycles, Airplanes, Faces, Leaves, Backgrounds
- Caltech 101
- Pictures of objects belonging to 101 categories
- Caltech 256
- Pictures of objects belonging to 256 categories
- Daimler Pedestrian Detection Benchmark
- 15,560 pedestrian and non-pedestrian samples (image cut-outs) and 6744 additional full images not containing pedestrians for bootstrapping. The test set contains more than 21,790 images with 56,492 pedestrian labels (fully visible or partially occluded), captured from a vehicle in urban traffic.
- MIT Pedestrian dataset
- CVC Pedestrian Datasets
- CVC Pedestrian Datasets
- CBCL Pedestrian Database
- MIT Face dataset
- CBCL Face Database
- MIT Car dataset
- CBCL Car Database
- MIT Street dataset
- CBCL Street Database