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Object Detection Using the Statistics of Parts

This paper presents a method for object detection using the statistics of parts. Key features include multiple exhaustive classifiers, parts-based representation with discretized wavelet coefficients, classifier design, and efficient exhaustive search techniques. The system is designed to estimate probabilities using AdaBoost with confidence-weighted predictions and a likelihood ratio test. The approach leverages the multi-resolution nature of wavelet coefficients to speed up computation and localize parts in space, frequency, and orientation. The method involves the collection and processing of object images, training with AdaBoost, and efficient exhaustive search across various parameters. While the approach works well, it requires manual intervention and is slow due to the exhaustive search process.

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Object Detection Using the Statistics of Parts

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  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

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

  7. 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

  8. 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

  9. 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

  10. 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)

  11. Results : Face DetectionSometimes it Works

  12. And Sometimes it Doesn’t

  13. Results : Car Detection

  14. DiscussionWhich are the Important Parts ?

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

  16. How many faces in this picture ?

  17. What about this ?

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