PASCAL VOC数据集The PASCAL Object Recognition Database Collection

The PASCAL Object Recognition Database Collection

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News

• 04-Apr-07: The VOC2007 challenge development kit is now available.

Objectives

• To compile a standardised collection of object recognition databases
• To provide standardised ground truth object annotations across all databases
• To provide a common set of tools for accessing and managing the database annotations

Standardising Each Database

• All images available in PNG format
• Annotated with ground truth
• Ground truth information in each annotated image includes a bounding box for the objects of interest and might also include pixel segmentation masks or polygonal boundaries. The class label provided by the authors who originally compiled the database is retained and a PASCAL class label added.

MATLAB Database Tools

• PASannotateimg.m - GUI tool to annotate an image and mark out objects of interest
• PASreadrecord.m - Load annotation information into MATLAB
• PASwriterecord.m - Write annotation information to disk
• PASviewannotation.m - Display annotated image and objects

The MATLAB database tools can be downloaded as tar.gz file

Jump to ...

1. Fully Annotated Databases
   1. The TU Darmstadt Database (formerly the ETHZ Database)
   2. The UIUC Image Database for Car Detection
   3. The VOC2005 Database: Dataset 1
   4. The VOC2005 Database: Testset 2
   5. The VOC2006 Database
2. Partially Annotated Databases
   1. The Caltech Database
   2. The MIT-CSAIL Database of Objects and Scenes
   3. The TU Graz-02 Database
3. Unannotated Databases
   1. 101 Object Categories

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Fully Annotated Databases

1.1   The TU Darmstadt Database (formerly the ETHZ Database)

Authors	Bastian Leibe and Bernt Schiele
Institute	Darmstadt University of Technology
Database URL	http://www.mis.informatik.tu-darmstadt.de/leibe
PASCAL download	Download tar.gz file of annotated PNG images
Categories	Side views of motorbikes, cars and cows
Number of images	115 motorbikes + 50 x 2 cars + 112 cows = 327
Number of annotated images	326 (cow-pic530-sml-lt discarded because of incorrect segmentation mask)
Object annotation statistics	125 PASmotorbikeSide objects + 100 PAScarSide objects + 111 PAScowSide objects
Annotation notes	The original ground truth data provided by the authors is given in terms of bounding boxes for the motorbikes and pixel segmentation masks for the cows and the cars  The original segmentation masks, which are non-binary RGB triplets, have been converted to PASCAL masks (0=background, 1=object) by converting the colour masks to grey scale values between 0 and 255 and then thresholding at 128
Browse	Browse all images
Acknowledgements	Cow data provided by Derek Magee, University of Leeds.  This work has been part of the CogVis project, funded in part by the Commission of the European Union (IST-2000-29375), and the Swiss Federal Office for Education and Science (BBW 00.0617)
Publications	B. Leibe, A. Leonardis and B. Schiele. Combined object categorization and segmentation with an implicit shape model. InProceedings of the Workshop on Statistical Learning in Computer Vision. Prague, Czech Republic, May 2004.  Bibtex source | Download in pdf format  D. Magee and R. Boyle. Detecting Lameness using "Re-sampling Condensation" and "Multi-stream Cyclic Hidden Markov Models." Image and Vision Computing, vol 20(8), pp 581-594, 2002.

Database variability and level of difficulty for objection recognition:

• The database has side views of 50 cars which have been mirrored to give a total of 100. All cars have roughly the same scale and occur in the centre of the image. There is only one car per image
• All cows have roughly the same scale and orientation (side view, facing left)
• The 111 cow images have only 3 distinct backgrounds and many of the cow images are quite similar to at least one other cow image in the database
• The motorbike images are more varied and include everyday scenes of people riding their bikes in cluttered environments
• The motorbikes appear at different scales, can have large illumination changes, can have partial occlusion and there can be multiple instances per image
• The annotations are fairly comprehensive as all visible cows and cars, and most motorbikes, have been labelled

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1.2   The UIUC Image Database for Car Detection

Authors	Shivani Agarwal, Aatif Awan and Dan Roth
Institute	University of Illinois at Urbana-Champaign
Database URL	http://l2r.cs.uiuc.edu/~cogcomp/Data/Car/
PASCAL download	Download tar.gz file of annotated PNG images
Categories	Side views of cars, negative examples (images without cars)
Number of images	550 training cars + 170 test cars + 108 multi-scale test cars + 500 negative training examples = 1328
Number of annotated images	1328
Object annotation statistics	889 PAScarSide objects + 500 PASbackground objects
Annotation notes	The original ground truth data provided by the authors is given in terms of bounding boxes for the cars  All cars have been assigned the label PAScarSide  Some test images have multiple cars  Following the original annotations, bounding boxes which lie outside the image have not been clipped  All negative examples have been assigned the label PASbackground (with the original label field being set to "Not carSide") and their bounding boxes have been set to the image dimensions
Browse	Browse all images
Acknowledgements	This research, including the collection of this database, was supported by NSF grants ITR IIS 00-85980 and ITR IIS 00-85836
Publications	S. Agarwal and D. Roth. Learning a sparse representation for object detection. In Proceedings of the European Conference on Computer Vision, volume 4, pages 113--130. Copenhagen, Denmark, May 2002. Springer-Verlag.  Bibtex source | Download in pdf format  S. Agarwal, A. Awan and D. Roth. Learning to detect objects in images via a sparse, part-based representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(11):1475--1490, November 2004.  Bibtex source | Download in pdf format

Database variability and level of difficulty for objection recognition:

• The database has only a single object category
• Only side views of cars are present and the database has no rotated or frontal / rear views
• The database does have 108 images with scale variations though the aspect ratio (y/x) of the bounding box always equals 0.4
• However, most cars in the database occur at a single fixed scale
• The images are fairly clean with little occlusion. However, there is a small amount of background clutter
• For the 550 training images, the car is always the dominant object present in the middle of the image and occurring at a fixed scale. Thus, these images are good for training, but not for testing.
• The annotations are comprehensive as all visible cars have been labelled

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1.3   The VOC 2005 Database: Dataset 1

Authors	Mark Everingham  (Compiled by)
Institute	University of Oxford
Database URL	http://www.pascal-network.org/challenges/VOC/databases.html
PASCAL download	Download tar.gz file of annotated PNG images
Categories	Views of motorbikes, bicycles, people, and cars in arbitrary pose.
Number of images	1578
Number of annotated images	1578
Object annotation statistics	Total number of labelled objects =  2209
Annotation notes	The images in this database are a subset of the other image databases on this page. The images were manually selected as an "easier" dataset for the 2005 VOC challenge. Annotations were taken verbatim from the source databases.
Browse	Browse all images
Acknowledgements	Images in this database were taken from the TU-Darmstadt, Caltech, TU-Graz and UIUC databases. Additional images were provided by INRIA. Funding was provided by PASCAL.
Publications	M. Everingham, A. Zisserman, C. K. I. Williams, L. Van Gool, et al. The 2005 PASCAL Visual Object Classes Challenge. InMachine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Textual Entailment., eds. J. Quinonero-Candela, I. Dagan, B. Magnini, and F. d'Alche-Buc, LNAI 3944, pages 117-176, Springer-Verlag, 2006.  Download in pdf format

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1.3   The VOC 2005 Database: Testset 2

Authors	Mark Everingham
Institute	University of Oxford
Database URL	http://www.pascal-network.org/challenges/VOC/databases.html
PASCAL download	Download tar.gz file of annotated PNG images
Categories	Views of motorbikes, bicycles, people, and cars in arbitrary pose.
Number of images	654
Number of annotated images	654
Object annotation statistics	Total number of labelled objects = 1293
Annotation notes	These images were collected from Google for the 2005 VOC challenge. The images were chosen to provide a "harder" test set for the challenge. All images are annotated with instances of all four categories: motorbikes, bicycles, people and cars.
Browse	Browse all images
Acknowledgements	Funding was provided by PASCAL.
Publications	M. Everingham, A. Zisserman, C. K. I. Williams, L. Van Gool, et al. The 2005 PASCAL Visual Object Classes Challenge. InMachine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Textual Entailment., eds. J. Quinonero-Candela, I. Dagan, B. Magnini, and F. d'Alche-Buc, LNAI 3944, pages 117-176, Springer-Verlag, 2006.  Download in pdf format

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1.4   The VOC 2006 Database

Authors	Mark Everingham  (Compiled by)
Institute	University of Oxford
Database URL	http://www.pascal-network.org/challenges/VOC/databases.html
PASCAL download	Download tar file of annotated PNG images: train+val sets  Download tar file of annotated PNG images: test set
License	By downloading the test data you are agreeing to abide by the licenses for the "flickr" and Microsoft Research Cambridge images contained in the database: "flickr" terms of use MSR Cambridge License (RTF) Details of the contributor of each image can be found in the file "contrib.txt" included in the database.
Categories	Views of bicycles, buses, cats, cars, cows, dogs, horses, motorbikes, people, sheep in arbitrary pose.
Number of images	5,304
Number of annotated images	5,304
Object annotation statistics	Total number of labelled objects = 9,507
Annotation notes	These images were collected from personal photographs, "flickr", and the Microsoft Research Cambridge database for the 2006 VOC challenge. All images are annotated with instances of all ten categories: bicycles, buses, cats, cars, cows, dogs, horses, motorbikes, people, sheep. Guidelines used for the annotation are available here.
Browse	Browse all images
Acknowledgements	Funding was provided by PASCAL. Images were contributed and/or annotated by Moray Allen, James Bednar, Matthijs Douze, Mark Everingham, Stefan Harmeling, Juan Huo, Lindsay Hutchison, Fiona Jamieson, Maria-Elena Nilsback, John Quinn, Florian Schroff, Kira Smyllie, Mark Van Rossum, Chris Williams, John Winn, Andrew Zisserman.
Publications	Bibtex source | Download in pdf format  M. Everingham, A. Zisserman, C. K. I. Williams, L. Van Gool. The 2006 PASCAL Visual Object Classes Challenge (VOC2006) Results. http://www.pascal-network.org/challenges/VOC/voc2006/results.pdf.

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Partially Annotated Databases

2.1   The Caltech Database

Authors	Rob Fergus and Pietro Perona
Institute	California Institute of Technology
Database URLs	http://www.robots.ox.ac.uk/~vgg/data3.html  http://www.vision.caltech.edu/html-files/archive.html
PASCAL download	Download tar.gz file of annotated PNG images
Categories	Profile views of aeroplanes and motorbikes, rear views of cars, car backgrounds, front views of faces and general background scenes
Number of images	1074 aeroplanes + 1155 cars + 450 faces + 826 motorbikes + 1370 car backgrounds + 900 general backgrounds = 5775
Number of annotated images	4620
Object annotation statistics	Total number of labelled objects = 1293
Annotation notes	The original ground truth data provided by the authors is given in terms of a bounding quadrilateral which is converted into a bounding rectangle  Following the original annotations, in some cases only one object per image is labelled even though there are multiple instances present.
Browse	Browse images selected for annotation
Acknowledgements	Funding was provided by the UK EPSRC; Caltech Center for Neuromorphic Systems Engineering and EC CogViSys project. Michalis Titsias annotated the car images.
Publications	R. Fergus, P. Perona and A. Zisserman. Object Class recognition by unsupervised scale-invariant learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 264--271. Madison, Wisconsin, June 2003. Bibtex source | Download in pdf format

Database variability and level of difficulty for objection recognition:

• The database has many images for each object allowing for sufficient intra-class variability
• However, there is very little pose, orientation, scale or illumination variation in the images
• There is almost no occlusion and only a limited number of images have background clutter
• The object of interest generally tends to occur in the middle of the image and can quite often fill the image
• For some categories only one object has been labelled per image even though there are multiple objects present

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2.2   The MIT-CSAIL Database of Objects and Scenes

Authors	Antonio Torralba, Kevin P. Murphy and William T. Freeman
Institute	Massachusetts Institute of Technology
Database URL	http://web.mit.edu/torralba/www/database.html
PASCAL download	Download tar.gz file of annotated PNG images: part 1, part 2
Categories	107 object classes and 18 region classes
Number of images	Over 72,000
Number of annotated images	2873
Object annotation statistics	Total number of labelled objects = 10,358.  View number of objects in each class
Annotation notes	The original ground truth data provided by the authors is given in terms of boundary polygons for each labelled object  Some very minor errors in the original annotations relating to image sizes and colour depth have been corrected  All the original object labels have been retained. Their PASCAL equivalents are obtained by adding a PAS in front of them  180 of the original annotated images which did not contain any objects have been assigned the label PASbackground
Browse	Browse, by subdirectory, images selected for annotation  Browse, by object class, images selected for annotation
Acknowledgements	Egon Pasztor made many contribution in the early stages of the database. We also want to give thanks to the flight delays and specially to the bad television programs who motivated us very much into annotating more images every day
Publications	A. Torralba, K. P. Murphy and W. T. Freeman. Sharing features: efficient boosting procedures for multiclass object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 762--769. Washington, DC, June 2004.  Bibtex source | Download in pdf format

Database variability and level of difficulty for objection recognition:

• The database has many different objects taken under a variety of viewing and illumination conditions
• The images have been taken from different sources such as web cams, digital cameras and over the web
• While the annotations are generally of a good quality, they can sometimes lack consistency in the labelling and some instances of objects have been missed altogether when performing the annotations

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2.3   The TU Graz-02 Database

Authors	Andreas Opelt and Axel Pinz
Institute	Graz University of Technology
Database URL	http://www.emt.tugraz.at/~pinz/data/GRAZ_02/
PASCAL download	Download tar.gz file of annotated PNG images
Categories	Bikes, cars, people and backgrounds
Number of images	365 bikes + 420 cars + 311 people + 380 negative examples = 1476
Number of annotated images	1280
Object annotation statistics	404 PASbicycle objects + 451 PAScar + 581 PASperson objects + 380 PASbackground objects
Annotation notes	The original ground truth data provided by the authors is given in terms of pixel segmentation masks  The original masks have values between 0 and 255. These are converted to PASCAL masks (0 = background, 1 = 1st object, 2 = 2nd object, etc.) by (a) setting all values greater than 0 as background, (b) finding the connected components of the foreground and (c) rejecting all components smaller than 100 pixels.  Following the original annotations, overlapping objects have been merged into a single object while disconnected objects parts have been labelled as separate objects.  The original negative examples had been labelled as "none". These are now labelled as PASbackground with the bounding box set to the dimensions of the image.
Browse	Browse images selected for annotation
Acknowledgements	The EU project LAVA (IST-2001-34405) and the Austrian Science Foundation (project S9103-N04)
Publications	A. Opelt, M. Fussenegger, A. Pinz and P. Auer. Generic object recognition with boosting. Technical Report TR-EMT-2004-01, EMT, TU Graz, Austria, 2004. Submitted to the IEEE Transactions on Pattern Analysis and Machine Intelligence.  Bibtex source | Download in pdf format  A. Opelt and A. Pinz. Object localization with boosting and weak supervision for generic object recognition. InProceedings of the 14th Scandinavian Conference on Image Analysis (SCIA).  Bibtex source | Download in pdfformat

Database variability and level of difficulty for objection recognition:

• The database has only three classes.
• However, images from each class display a large variability in scale, viewpoint and illumination.
• There are significant occlusions and background clutter.
• There is a large intra-class variability within the objects.
• Often, the objects of interest are not the dominant objects in the scene.
• The annotations are quite comprehensive and most objects of interest have been marked.

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Unannotated Databases

3.1   101 Object Categories

Authors	Fei-Fei Li, Marco Andreetto and Marc'Aurelio Ranzato
Institute	California Institute of Technology
Database URL	http://www.vision.caltech.edu/feifeili/101_ObjectCategories/
PASCAL download	Download tar.gz file of unannotated PNG images
Categories	101 object classes
Number of images	9197
Browse	Browse unannotated images from the database
Publications	L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In Proceedings of the Workshop on Generative-Model Based Vision. Washington, DC, June 2004.  Bibtex source | Download in pdf format

   

   

from: http://host.robots.ox.ac.uk/pascal/VOC/databases.html#VOC2005_1