Agenda Introduction Bag-of-words models Visual words with spatial location Part-based models Discriminative methods Segmentation and recognition Recognition-based image retrieval Datasets & Conclusions Databases Caltech 101 Caltech 256 Pascal Visual Object Classes (VOC) LabelMe Slides from Andrew Zisserman 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. Collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc 'Aurelio Ranzato. Train on 5, 10, 15, 20 or 30 images Test on rest report results per class Caltech 101 images Caltech-101: Drawbacks Smallest category size is 31 images: Too easy? left-right aligned Rotation artifacts Soon will saturate performance N train 30
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 traffic light kayac basketball-hoop dog baseball-bat Caltech 256 images The PASCAL Visual Object Classes
(VOC) Dataset and Challenge Mark Everingham Luc Van Gool Chris Williams John Winn Andrew Zisserman The PASCAL VOC Challenge Challenge in visual object recognition funded by PASCAL network of excellence Publicly available dataset of annotated images. Development kit available. Main competitions in classification (is there an X in this image) and detection (where are the Xs) Taster competitions in segmentation and 2-D human pose estimation (2007-present) 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 Occluded Object is significantly occluded within BB Truncated Object extends beyond BB Difficult
Not scored in evaluation Pose Facing left Examples Aeroplane Bicycle Bus Car Bird Cat Boat
4 12 Collection of existing and some new data. 2006 5,304 9,507 10 25 Completely new dataset from flickr (+MSRC)
2007 9,963 24,640 20 28 Increased classes to 20. Introduced tasters. 2008 8,776 20,739 20 Added occlusion flag. Reuse of taster data. Release detailed results to support meta-analysis
New dataset annotated annually Annotation of test set is withheld until after challenge Main Challenge Tasks Classification Is there a dog in this image? Evaluation by precision/recall Detection Localize all the people (if any) in this image Evaluation by precision/recall based on bounding box overlap Example Precision/Recall: 2007 Person detection 1 IRISA (0.221) UoCTTI (0.213)
LabelMe Russell, Torralba, Freman, 2005 Links to datasets The next tables summarize some of the available datasets for training and testing object detection and recognition algorithms. These lists are far from exhaustive. Databases for object localization CMU/MIT frontal faces vasc.ri.cmu.edu/idb/html/face/frontal_images cbcl.mit.edu/software-datasets/FaceData2.html Patches Frontal faces Graz-02 Database www.emt.tugraz.at/~pinz/data/GRAZ_02/
ESP game www.espgame.org Global image descriptions Web images LabelMe people.csail.mit.edu/brussell/research/LabelMe/intro.html Polygonal boundary High resolution images http://www.pascal-network.org/challenges/VOC/ Segmentation, boxes various
Collections PASCAL Topics not covered Context Scene Inter-object relations Video Tracking & detection Multiple viewpoints Summary Methods reviewed here
Bag of words Bag of words with location Parts and structure Discriminative methods Combined Segmentation and recognition Recognition for retrieval Resources online: http://cs.nyu.edu/~fergus/icml_tutorial Slides Code Links to datasets
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