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 h t (x) with weighted training samples. –First Classifier h 1 (x) – equal weights to all. –Next – Higher weight to Incorrectly classified samples –Final Classifier: –α t 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 Detection Sometimes it Works

13. And Sometimes it Doesn’t

14. Results : Car Detection

15. Discussion Which 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 ?