1. Predicting Matchability - CVPR 2014 Paper -
Min-Gyu Park
Computer Vision Lab.
School of Information and Communications
GIST

2. Intro (1/3) Point feature extraction and matching
Initial step for various computer vision algorithms
SFM, object recognition, feature-based tracking…
SIGGRAPH 2014, Feature Matching with Bounded Distortion

3. Intro (2/3) Millions of images!!!
Feature extraction and matching step can be the bottleneck for large-scale applications
SFM from collected images from the web, Bundler, Microsoft
Millions of images!!!

4. Intro (3/3) Possible approaches to reduce the time complexity
Reduce the number of images
Make a cluster of images and select a representative image
Developing fast matching methods
Approximate Nearest Neighbor (ANN), k-NN, etc
Extract fewer numbers of features
This paper belongs to this category

5. Predicting Matchability (1/3)
Predict matchable features priori to matching!
To remove un-matchable features
The simplest approach might be,
Reject if the detector response is weak
Cornerness is less than a user-defined threshold value
However, repeatable and well-localized points do not guarantee they are “matched correctly”

6. Predicting Matchability (2/3)
Goal
Train a classifier to predict matchable features
As well as to rule out un-matchable features!
Training step
Classifier is trained in the Random Forest framework
SIFT descriptors (extracted from training images)
Positive samples (485,000)
Negative samples (485,000)

7. Predicting Matchability (3/3)
Testing step
Run down each tree in the random forest
Proposed results

8. Experiment Detection performance
Accuracy of prediction in terms of ROC curves
Qualitative evaluation
SFM results with predictable matches

9. Detection performance
ROC curves for three datasets
Green: proposed
Blue: DoG threshold
Red: random selection
(d) Confusion bars

10. SFM with Proposed Method
The proposed method better recovers the shape of the object properly

11. Q & A