Object Detection Using the Statistics of Parts

1. Object Detection Using the Statistics of Parts Henry Schneiderman Takeo Kanade Presented by : Sameer Shirdhonkar December 11, 2003

2. Overview Main Features of Paper Multiple Exhaustive Classifiers Parts based representation :Discretized Wavelet Coefficients Estimating probabilities :AdaBoost with Confidence Weighted Predictions

3. 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

4. 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)

5. Approximations • Parts are statistically Independent – Localized Dependence for cars, faces, etc. • Part values (Wavelet Transform coefficients) are quantized • Part positions are quantized coarsely

7. Local Operators Locality in position more important Local Operator – Moving Combination of Wavelet coefficients

8. 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

9. 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

10. 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

11. 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)

12. Results : Face DetectionSometimes it Works

13. And Sometimes it Doesn’t

14. Results : Car Detection

15. DiscussionWhich are the Important Parts ?

16. Conclusion • Works pretty well • Training is difficult and needs too much manual intervention • Slow – due to exhaustive search

17. How many faces in this picture ?

19. What about this ?