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Summary

Recognizing and Learning Object categories. Summary. Summary. Methods reviewed here Bag of words Parts and structure Discriminative methods Combined Segmentation and recognition Resources online Slides Code Links to datasets. http://people.csail.mit.edu/torralba/iccv2005/.

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Summary

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  1. Recognizing and Learning Object categories Summary

  2. Summary • Methods reviewed here • Bag of words • Parts and structure • Discriminative methods • Combined Segmentation and recognition • Resources online • Slides • Code • Links to datasets http://people.csail.mit.edu/torralba/iccv2005/

  3. List properties of ideal recognition system • Representation • 1000’s categories, • Handle all invariances (occlusions, view point, …) • Explain as many pixels as possible (or answer as many questions as you can about the object) • fast, robust • Learning • Handle all degrees of supervision • Incremental learning • Few training images • …

  4. Online resources http://people.csail.mit.edu/torralba/iccv2005/

  5. 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 Databases for object recognition On-line annotation tools Collections

  6. LabelMe Dataset • There are about 19,500 labelled objects http://www.csail.mit.edu/~brussell/research/LabelMe/intro.html Google search: LabelMe MIT

  7. LabelMe Screen Shot

  8. Matlab toolbox LMquery (database, 'object.name', 'car,building,road,tree')

  9. Toolbox LMquery (database, 'object.name', 'car,building,road,tree') LMcookdatabase (database, 'objectname', 'screen', … 'objectsize', [64 64], 'objectlocation', 'original','maximagesize', [128 128])

  10. Example Annotations

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