EECS 442 – Computer vision Segments of this lectures are courtesy of Prof F. Li, R. Fergus and A. Zisserman Databases for object recognition and beyond.

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Presentation transcript:

EECS 442 – Computer vision Segments of this lectures are courtesy of Prof F. Li, R. Fergus and A. Zisserman Databases for object recognition and beyond

Caltech 101 Pictures of objects belonging to 101 categories. About 40 to 800 images per category. Most categories have about 50 images. The size of each image is roughly 300 x 200 pixels.

Caltech 101 images

Smallest category size is 31 images: Too easy? –left-right aligned –Rotation artifacts –Saturated performance Caltech-101: Drawbacks

Results up to recent methods obtain almost 100%

Caltech-256 Smallest category size now 80 images About 30K images Harder –Not left-right aligned –No artifacts –Performance is halved –More categories New and larger clutter category

Caltech 256 images baseball-bat basketball-hoop dog kayac traffic light

The PASCAL Visual Object Classes (VOC) Dataset and Challenge Mark Everingham Luc Van Gool Chris Williams John Winn Andrew Zisserman

Dataset Content 20 classes: aeroplane, bicycle, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, train, TV Real images downloaded from flickr, not filtered for “quality” Complex scenes, scale, pose, lighting, occlusion,...

Annotation Complete annotation of all objects Annotated in one session with written guidelines Truncated Object extends beyond BB Occluded Object is significantly occluded within BB Pose Facing left Difficult Not scored in evaluation

Examples Aeroplane Bus BicycleBirdBoatBottle CarCatChairCow

History New dataset annotated annually –Annotation of test set is withheld until after challenge ImagesObjectsClassesEntries 20052,2322, Collection of existing and some new data ,3049, Completely new dataset from flickr (+MSRC) 20079,96324, Increased classes to 20. Introduced tasters ,77620,73920 Added “occlusion” flag. Reuse of taster data. Release detailed results to support “meta-analysis”

Other recent datasets ESP [Ahn et al, 2006] LabelMe [ Russell et al, 2005] TinyImage Torralba et al Lotus Hill [ Yao et al, 2007] MSRC [Shotton et al. 2006]

3D object dataset [Savarese & Fei-Fei 07]

Instances Poses 1012 … 1 72 … ……… … … …… 8 azimuth angles 3 zenith 3 distances ~ 7000 images!

~20K categories; 14 million images; ~700im/categ; free to public at 16 Largest dataset for object categories up to date J. Deng, H. Su. K. Li, L. Fei-Fei,

is a knowledge ontology Taxonomy Partonomy The “social network” of visual concepts –Hidden knowledge and structure among visual concepts –Prior knowledge –Context

More Datasets…. UIUC Cars (2004) S. Agarwal, A. Awan, D. Roth 3D Textures (2005) S. Lazebnik, C. Schmid, J. Ponce CuRRET Textures (1999) K. Dana B. Van Ginneken S. Nayar J. Koenderink CAVIAR Tracking (2005) R. Fisher, J. Santos-Victor J. Crowley FERET Faces (1998) P. Phillips, H. Wechsler, J. Huang, P. Raus CMU/VASC Faces (1998) H. Rowley, S. Baluja, T. Kanade MNIST digits ( ) Y LeCun & C. Cortes KTH human action (2004) I. Leptev & B. Caputo Sign Language (2008) P. Buehler, M. Everingham, A. Zisserman Segmentation (2001) D. Martin, C. Fowlkes, D. Tal, J. Malik. Middlebury Stereo (2002) D. Scharstein R. Szeliski COIL Objects (1996) S. Nene, S. Nayar, H. Murase

CMU/MIT frontal facesvasc.ri.cmu.edu/idb/html/face/frontal_images cbcl.mit.edu/software-datasets/FaceData2.html PatchesFrontal faces Graz-02 Databasewww.emt.tugraz.at/~pinz/data/GRAZ_02/Segmentation masksBikes, cars, people UIUC Image Databasel2r.cs.uiuc.edu/~cogcomp/Data/Car/Bounding boxesCars TU Darmstadt Databasewww.vision.ethz.ch/leibe/data/Segmentation masksMotorbikes, cars, cows LabelMe datasetpeople.csail.mit.edu/brussell/research/LabelMe/intro.htmlPolygonal boundary>500 Categories Caltech 101www.vision.caltech.edu/Image_Datasets/Caltech101/Caltech101.htmlSegmentation masks101 categories Caltech 256 COIL www1.cs.columbia.edu/CAVE/research/softlib/coil-100.html Bounding Box Patches 256 Categories 100 instances NORBwww.cs.nyu.edu/~ylclab/data/norb-v1.0/Bounding box50 toys Databases for object localization Databases for object recognition On-line annotation tools ESP gamewww.espgame.orgGlobal image descriptionsWeb images LabelMepeople.csail.mit.edu/brussell/research/LabelMe/intro.htmlPolygonal boundaryHigh resolution images The next tables summarize some of the available datasets for training and testing object detection and recognition algorithms. These lists are far from exhaustive. Links to datasets Collections PASCALhttp:// boxesvarious