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
Object detection using a max-margin Hough Transform
Our Approach: Hough Transform
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Popular for detecting parameterized shapes
Hough’59, Duda&Hart’72, Ballard’81,…
Local parts vote for object pose
Complexity : # parts * # votes
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
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|…)
To Do :
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.
Object detection using a max-margin Hough Transform
Learning Feature Weights : Second Try
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Position Posterior
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
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
Discriminative dictionaries (two talks ago, Gall et.al.’09)
Weights on codewords (our work)
Our approach directly optimizes detection
performance using a max-margin formulation
Any weak predictor of object center can be used is
this framework
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