Object Detection Using the
Statistics of Parts
Henry Schneiderman
Takeo Kanade
Presented by : Sameer Shirdhonkar
December 11, 2003
Overview
Main Features of Paper
• Multiple Exhaustive Classifiers
• Parts based representation :
Discretized Wavelet Coefficients
• Estimating probabilities :
AdaBoost with Confidence Weighted
Predictions
Classifier Design
• Part : Set of input features which are statistically
inter-dependent, and independent of other features.
• Wavelet Coefficients as Features: Linear Phase 5/3
perfect reconstruction filter bank
– Invertible transform [ but not after quantization ]
– Partially decorrelates natural scenes – less features
needed
– Parts can be localized by space, frequency and
orientation
– Multiresolution nature speeds up computation
Classifier Form
• Likelihood Ratio Test [ Used similar to SPRT ]
• Generalization of Ideal Classifier Table
[ Object present/absent for all possible feature values ]
• Convert P(Image|Object) and P(Image|Non-Object) to P(object|mage)
• Change P(Object|Image) to Classifier output (present/absent)
Approximations
• Parts are statistically Independent –
Localized Dependence for cars, faces, etc.
• Part values (Wavelet Transform
coefficients) are quantized
• Part positions are quantized coarsely
Local Operators
• Locality in position more important
• Local Operator – Moving Combination of Wavelet
coefficients
Local Operator Design
• Intra-subband operators – 6
– Joint localization in space, frequency and orientation
• Inter-Orientation operators – 4
– Localization in space and frequency, different
orientations
• Inter-frequency operators – 6
– Localization in space and orientation, broad frequency
content
• Inter-Orientation + Inter-Frequency Operator – 1
– Localization in space, different frequency and
orientation
The Hard Part: Collecting Data
• Pre-processing Object Images:
– Size normalization and Spatial Alignment
– Intensity Normalization and Lighting
Correction – Separate normalizations for left
and right parts of face (5 discrete values)
– Synthesizing data : Positional perturbation,
Overcomplete evaluation of wavelet transform,
background substitution, low pass filtering
• Non-object images : Bootstrapping
Training
• Probabilistic Approximation
– Filling the histogram bins of Parts
• AdaBoost :
– Train Multiple Classifiers ht(x) with weighted training
samples.
– First Classifier h1(x) – equal weights to all.
– Next – Higher weight to Incorrectly classified samples
– Final Classifier:
– αt found by binary search
– The weighted sum of classifiers is reduced to a single
classifier due to linearity (in log likelihood).
Efficient Exhaustive Search
[Does this exist ?]
• Algorithm uses exhaustive search across position,
size, orientation, alignment and intensity.
• Course to Fine Evaluation – similar to SPRT
• Wavelet Transform coefficients can be reused for
multiple scales
• Color preprocessing
• Time – 5 s for 240x256 image (PII 450 MHz)
Results : Face Detection
Sometimes it Works
And Sometimes it Doesn’t
Results : Car Detection
Discussion
Which are the Important Parts ?
Conclusion
• Works pretty well
• Training is difficult and needs too much
manual intervention
• Slow – due to exhaustive search
How many faces in this picture ?
What about this ?