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Context-based vision system for place and object recognition. Antonio Torralba Kevin Murphy Bill Freeman Mark Rubin Presented by David Lee. Some slides borrowed from Kevin Murphy. Object out of context. Object in context. Wearable test-bed. System diagram. Computing the features.

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context based vision system for place and object recognition

Context-based vision system for place and object recognition

Antonio Torralba

Kevin Murphy

Bill Freeman

Mark Rubin

Presented by David Lee

Some slides borrowed from Kevin Murphy

slide7

4x4x24

=384 dim

80 dim

Downsample

to 4x4

24 filtered

Images

visualizing the filter bank output
Visualizing the filter bank output

Images

80-dimensional representation

hidden markov model
Hidden Markov Model
  • Hidden states = location (63 values)
  • Observations = vGt∈ R80
  • Transition model encodes topology of environment
  • Observation model is a mixture of Gaussians (100 views per place)
hidden markov model1

Mixture of Gaussians

MLE (counting)

Hidden Markov Model

Observation Likelihood

Prediction Prior

Transition Matrix

scene categorization
Scene Categorization
  • 17 Categories (Office, Corridor, Street, etc)
  • Train a separate HMM on category labels
performance on known env
Performance on known env.

Ground truth

System estimate

Specific location

Location category

Indoor/outdoor

comparison of features
Comparison of features

Categorization

Recognition

effect of hmm on recognition
Effect of HMM on recognition

Without

With

(But with temporal smoothing)

object priming
Object priming
  • Predict object properties based oncontext (top-down signals):
    • Visual gist, vtG
    • Specific Location, Qt
    • Kind of location, Ct
object priming1

MLE

Mixture of Gaussians

Object Priming

Estimate of current place

(Output of HMM)

Probability of object i in image vi given entire video sequence

Probability of object i

Given current observation & place

Prior probability of object i being in place q

Observation Likelihood

Probability of object i

Again…

predicting object position and scale1
Predicting object position and scale

Probability of an object i being present and location being q

(Output of previous system)

Estimate of mask

Estimate of mask given current gist, place, and object

delta

Gaussian

conclusion
Conclusion
  • Real world problem (and it works!)
  • Uses only global feature (context)
  • How much did {HMM / place prior} affect{place recognition / object detection}?Can we really say “context” did the job?