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)

• Feature extraction and matching step can be the bot-tleneck 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 complex-ity– 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: proposedBlue: DoG thresholdRed: random selection

(d) Confusion bars

SFM with Proposed Method

• The proposed method better recovers the shape of the object properly

Q & A