Iterative Projection and Matching

implementation of the data selection algorithm proposed in:

Alireza Zaeemzadeh, Mohsen Joneidi ( shared first authorship) , Nazanin Rahnavard, Mubarak Shah: Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer Vision. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. link

• For a quick demo using MNIST, please run python demo.py.
• For active learning experiments on UCF101 video dataset see here.

Requirements

irlb: Truncated SVD by implicitly restarted Lanczos bidiagonalization for Numpy! code

Visualization

t-SNE visualization of two classes of UCF-101 dataset and their representatives selected by IPM. (left) Decision function learned by using all the data. The goal of selection is to preserve the structure with only a few representatives. (right) Decision function learned by using representatives selected by IPM.

Citing IPM

If you use IPM in your research, please use the following BibTeX entry.

@inproceedings{zaeemzadeh2019ipm,
    title = {{Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer Vision}},
    year = {2019},
    booktitle = {Computer Vision and Pattern Recognition, 2019. CVPR 2019. IEEE Conference on},
    author = {Zaeemzadeh, Alireza and Joneidi, Mohsen and Rahnavard, Nazanin and Shah, Mubarak}
}

Project Webpages

Presentation

UCF Center for Research in Computer Vision (CRCV)

UCF Communications and Wireless Networks Lab (CWNlab)

Active Learning on UCF101 using IPM