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).– Use Cross Validation to prevent overfitting

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 DetectionSometimes it Works

And Sometimes it Doesn’t

Results : Car Detection

DiscussionWhich 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 ?