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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. - PowerPoint PPT Presentation
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Context-based vision system for place and object recognition
Antonio TorralbaKevin MurphyBill FreemanMark Rubin
Presented by David LeeSome slides borrowed from Kevin Murphy
Object out of context
Object in context
Wearable test-bed
System diagram
Computing the features
24 filteredImages
Downs
ampl
e
to 4
x4
4x4x24=384 dim 80 dim
Visualizing the filter bank output
Images
80-dimensional representation
Place recognition system
Hidden Markov Model
Hidden states = location (63 values) Observations = vG
t ∈ R80
Transition model encodes topology of environment
Observation model is a mixture of Gaussians (100 views per place)
Hidden Markov Model
Observation Likelihood
Prediction Prior
Transition Matrix
Mixture of Gaussians MLE (counting)
Scene Categorization
17 Categories (Office, Corridor, Street, etc)
Train a separate HMM on category labels
Place recognition demo
Specific location
Location category
Indoor/outdoor
Ground truthSystem estimate
Performance on known env.
Performance on new env.
Comparison of features
Recognition Categorization
Effect of HMM on recognition
With Without(But with temporal smoothing)
From place to object recognition
Object priming Predict object properties based on
context (top-down signals): Visual gist, vt
G
Specific Location, Qt
Kind of location, Ct
Object Priming
Again…MLE
Probability of object i
Probability of object i in image vi given entire video
sequence
Probability of object i Given current
observation & place
Estimate of current place
(Output of HMM)
Mixture of Gaussians
Observation Likelihood
Prior probability of object i being
in place q
Predicting object presence
ROC curves for object detection
Predicting object position and scale
Predicting object position and scaleEstimate of
mask
Probability of an object i being present and location being q(Output of previous system)
Estimate of mask given current gist, place, and object
delta Gaussian
Predicted segmentation
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?