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Authors : Antonio Torralba, Kevin P. Murphy, William T. Freeman, and Mark A. Rubin

An opposition to: Context-Based Vision System for Place and Object Recognition Contextual Models for Object Detection Using BRFs. Authors : Antonio Torralba, Kevin P. Murphy, William T. Freeman, and Mark A. Rubin Opponent : Carlos Vallespi. Paper claims.

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Authors : Antonio Torralba, Kevin P. Murphy, William T. Freeman, and Mark A. Rubin

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  1. An opposition to: Context-Based Vision System for Place and Object Recognition Contextual Models for Object Detection Using BRFs Authors: Antonio Torralba, Kevin P. Murphy, William T. Freeman, and Mark A. Rubin Opponent: Carlos Vallespi

  2. Paper claims • Claims to recognize 63 different locations. • Claims to categorize new environments • Claims to help object recognition by suggesting presence and location.

  3. Place recognition Is the classifier really doing anything? • Temporal information is available. • HMM will help a lot to the classifier. • Only 2-3 choices are possible at a time, knowing the current state.

  4. Simple place recognition with SIFT Database

  5. Simple place recognition with SIFT Test DB

  6. Comparing with SIFT 74 matches

  7. Comparing with SIFT Some correct matches

  8. Comparing with SIFT Correct no matches

  9. Comparing with SIFT • No incorrect mismatches • Just one weak match (22 matches): • Provided 9 locations and 100% accuracy in the test set.

  10. Scene categorization • This paper claims that they are able to categorize 17 unseen scenarios. • We have seen other methods in the past for scene categorization that also worked well (with up to 13 classes): • Bag-of-words approaches (using textons, for instance). • Histogram-based approaches. • Torralba’s paper (using image frequencies). • They use an average of local features over the image with a sliding window. • In fact, this is just a sort of histogram approach (nothing new). • DB does not seem very generic. They do not compare with other methods. • It performs poorly, except for the exception of the HMM:

  11. ??? Object presence and location • Their own images speak for themselves ;) • A filecabinet is expected to be seen in almost the entire image. • Most of the objects that are highly expected to be found, do not show up.

  12. Object presence and location • Their own images speak for themselves ;) • Except for the case of the building (which I am sure I could get something similar by averaging all the bounding boxes of buildings), all others are wrong… even the sky.

  13. Conclusions • Place recognition: • It seems to be an easy problem, that can be solved by simpler methods without temporal information. • An HMM alone could have done similar work. • Scene categorization: • Suspicious DB • Only works because of the temporal information. • Object presence and location: • Just does not work.

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