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Exploiting Hierarchical Context on a Large Database of Object Categories

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Exploiting Hierarchical Context on a Large Database of Object Categories -- Paper Presentation

Exploiting Hierarchical Context on a Large Database of Object Categories -- Paper Presentation

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  • 1. Exploiting Hierarchical Context on a Large Database of Object Categories
    Myung Jin Choi, Joseph J. Lim, Antonio Torralba, Alan S. Willsky
    Proceedings of CVPR-2010
  • 2. The SUN 09 Dataset
    • 12,000 annotated images (indoors and outdoors)
    • 3. Large number of scene categories, 200 object categories, 152,000 annotated object instances (using LabelMe)
    • 4. Average object size is 5% of the image size
    • 5. A typical image contains 7 different object categories
    PASCAl 07
    SUN 09
  • 6. Tree-structured Context Model
    Context Model
    Prior Model
    Measurement Model
    Co-occurrences Prior
    Spatial Prior
    Global Image Features
    Local Detector Outputs
  • 7. Prior Model
    Co-occurrences Prior: Encodes the co-occurrence statistics using a binary tree model
    Spatial Prior: Captures information regarding the specific relative positions among appearance of objects
  • 8. Prior on Spatial Locations
    • Given L-x, L-y and L-z as any object’s location in the 3D world co-ordinate, L-x is ignored (being uninformative), L-y is modeled as jointly Gaussian and L-z as Log-normal distribution.
    • 9. Location variable: L-i = (L-y, log L-z)
    • 10. L-i’s are modeled as jointly Gaussian and in case of multiple instances of the same category, L-I represent the median location of all instances.
    The joint distribution of all binary and Gaussian variables is finally represented as:
  • 11. Measurement Model
    Incorporating Global Image Features: Uses gist to measure the presence of an object in an image (scene)
    Integrating Local Detector Outputs: Taking the candidate windows from a baseline object detector, and learning the likelihood of their correct detection from the training set, the expected location of an object is obtained.
  • 12. Alternating Inference
    Given the gist g, candidate window locations W and their scores s, the algorithm infers the presence of objects b, the correct detection c and expected location of objects L, by solving the optimization problem:
  • 13. Learning the dependency
    The dependency structure among objects is learnt from a set of fully labeled images using the Chow-Liu algorithm.
    • It computes the empirical mutual information of all pairs of variables (using sample values in the set of labeled images)
    • 14. It then finds the maximum weight spanning tree with edge weights equal to the mutual information
    • 15. A root node is arbitrarily selected once a tree structure is learned.
  • Learning the dependency
  • 16. Results
    Performance on Pascal 07
    Object Recognition Performance
  • 17. Results
    Performance on SUN 09
    Image Annotation Performance
  • 18. Results
    Performance on SUN 09
  • 19. Detecting Images out of context
  • 20. Detecting Images out of context
    • Database: 26 images with one/more objects out of context
    • 21. All objects have ground-truth object labels, except for the one under the test.
    • 22. The context model correctly identifies the most unexpected object in the scene.
  • Conclusion
    • The new dataset SUN 09 contains richer contextual information compared to PASCAL 07, which was originally designed for training object detectors.
    • 23. The paper demonstrates that the contextual information learned from SUN 09 significantly improves the accuracy of object recognition tasks, and can even be used to identify out-of-context scenes.
    • 24. The tree-based context model enables an efficient and coherent modeling of regularities among object categories, and can easily scale to capture dependencies of over 100 object categories.

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