Transcript Slide 1

Object Detection Using a
Max-Margin Hough Transform
Subhransu Maji and Jitendra Malik
University of California at Berkeley, Berkeley, CA-94720
CVPR 2009, Miami, Florida
Overview
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Overview of probabilistic Hough transform
 Learning framework
 Experiments
 Summary
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Object detection using a max-margin Hough Transform
Our Approach: Hough Transform
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Popular for detecting parameterized shapes
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Hough’59, Duda&Hart’72, Ballard’81,…
Local parts vote for object pose
 Complexity : # parts * # votes
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Can be significantly lower than brute force search over
pose (for example sliding window detectors)
Object detection using a max-margin Hough Transform
Generalized to object detection
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 Use Hough space voting to find objects
Lowe’99, Leibe et.al.’04,’08, Opelt&Pinz’08
 Implicit Shape Model
Leibe et.al.’04,’08
Learning
• Learn appearance codebook
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– Cluster over interest points on
training images
• Learn spatial distributions
– Match codebook to training images
– Record matching positions on object
– Centroid is given
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Spatial occurrence distributions
Object detection using a max-margin Hough Transform
Detection Pipeline
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Interest Points
Matched Codebook
Entries
eg. SIFT,GB, Local Patches
KD Tree
Probabilistic
Voting
B. Leibe, A. Leonardis, and B. Schiele.
Combined object categorization and segmentation with an implicit shape model ‘ 2004
Object detection using a max-margin Hough Transform
Probabilistic Hough Transform
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C – Codebook
 f – features, l - locations
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Detection Score
Position Posterior
Codeword Match
Codeword likelihood
Object detection using a max-margin Hough Transform
Learning Feature Weights
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Given :
Appearance Codebook, C
 Posterior distribution of object center for each codeword
P(x|…)
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To Do :
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Learn codebook weights such that the Hough transform
detector works well (i.e. better detection rates)
Contributions :
Show that these weights can be learned optimally using a
max-margin framework.
2. Demonstrate that this leads to improved accuracy on
various datasets
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Object detection using a max-margin Hough Transform
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Learning Feature Weights : First Try
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
Naïve Bayes weights:
Encourages relatively rare parts
 However rare parts may not be good predictors of
the object location
 Need to jointly consider both priors and distribution
of location centers.
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Object detection using a max-margin Hough Transform
Learning Feature Weights : Second Try
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Position Posterior
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Codeword Match
Codeword likelihood
Location invariance assumption
Overall score is linear given the matched codebook entries
Feature weights
Activations
Object detection using a max-margin Hough Transform
Max-Margin Training
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class label {+1,-1}
activations
non negative
Standard ISM model
(Leibe et.al.’04)
Training:
1.Construct dictionary
2.Record codeword distributions on training examples
3.Compute “a” vectors on positive and negative training examples
4.Learn codebook weights using by max-margin training
Our Contribution
Object detection using a max-margin Hough Transform
Experiment Datasets
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ETHZ Shape Dataset (Ferrari et al., ECCV 2006)
255 images, over 5 classes (Apple logo, Bottle, Giraffe, Mug, Swan)
UIUC Single Scale Cars Dataset (Agarwal & Roth, ECCV 2002)
1050 training, 170 test images
INRIA Horse Dataset (Jurie & Ferrari)
170 positive + 170 negative images (50 + 50 for training)
Object detection using a max-margin Hough Transform
Experimental Results
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
Hough transform details
Interest points : Geometric Blur descriptors at sparse
sample of edges (Berg&Malik’01)
 Codebook constructed using k-means
 Voting over position and aspect ratio
 Search over scales
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Correct detections (PASCAL criterion)
Object detection using a max-margin Hough Transform
Learned Weights (ETHZ shape)
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Naïve Bayes
Max-Margin
Influenced by clutter
(rare structures)
Important Parts
blue (low) , dark red (high)
Object detection using a max-margin Hough Transform
Learned Weights (UIUC cars)
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Naïve Bayes
Max-Margin
Important Parts
blue (low) , dark red (high)
Object detection using a max-margin Hough Transform
Learned Weights (INRIA horses)
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Naïve Bayes
Max-Margin
Important Parts
blue (low) , dark red (high)
Object detection using a max-margin Hough Transform
Detection Results (ETHZ dataset)
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Recall @ 1.0 False Positives Per Window
Object detection using a max-margin Hough Transform
Detection Results (INRIA Horses)
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Our Work
Object detection using a max-margin Hough Transform
Detection Results (UIUC Cars)
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Our Work
INRIA horses
Object detection using a max-margin Hough Transform
Hough Voting + Verification Classifier
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Recall @ 0.3 False Positives Per Image
ETHZ Shape Dataset
Implicit sampling
IKSVM was run on top 30 windows + local
over search
aspect-ratio
KAS – Ferrari et.al., PAMI’08
better fitting bounding box
TPS-RPM – Ferrari et.al., CVPR’07
Object detection using a max-margin Hough Transform
Hough Voting + Verification Classifier
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Our Work
IKSVM was run on top 30 windows + local search
Object detection using a max-margin Hough Transform
Hough Voting + Verification Classifier
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1.7% improvement
UIUC Single Scale Car Dataset
IKSVM was run on top 10 windows + local search
Object detection using a max-margin Hough Transform
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Summary
Hough transform based detectors offer good
detection performance and speed.
 To get better performance one may learn
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Discriminative dictionaries (two talks ago, Gall et.al.’09)
 Weights on codewords (our work)
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Our approach directly optimizes detection
performance using a max-margin formulation
 Any weak predictor of object center can be used is
this framework
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Eg. Regions (one talk ago, Gu et.al. CVPR’09)
Object detection using a max-margin Hough Transform
Thank You
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Acknowledgements
Work partially supported by:
ARO MURI W911NF-06-1-0076 and ONR MURI N00014-06-1-0734
Computer Vision Group @ UC Berkeley
Questions?
Backup Slide : Toy Example
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Rare but poor localization
Rare and good localization
Object detection using a max-margin Hough Transform