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

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

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!!!

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

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”

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)

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

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

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

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

Q & A