Analysis Dictionary Learning Based Classification: Structure for Robustness

This repository contains the demo and MATLAB codes for our IEEE TIP paper: "Analysis Dictionary Learning Based Classification: Structure for Robustness" by Wen Tang, Ashkan Panahi, Hamid Krim and Liyi Dai. The Algorithm proofs can be found in the supplementary material.

This is also the journal version of the conference paper "Structured Analysis Dictionary Learning for Image Classification", which is published in ICASSP 2018.

Citation

If you think our projects are useful, please consider citing them:

@article{tang2019analysis,
  title={Analysis dictionary learning based classification: Structure for robustness},
  author={Tang, Wen and Panahi, Ashkan and Krim, Hamid and Dai, Liyi},
  journal={IEEE Transactions on Image Processing},
  volume={28},
  number={12},
  pages={6035--6046},
  year={2019},
  publisher={IEEE}
}

@inproceedings{tang2018structured,
  title={Structured analysis dictionary learning for image classification},
  author={Tang, Wen and Panahi, Ashkan and Krim, Hamid and Dai, Liyi},
  booktitle={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={2181--2185},
  year={2018},
  organization={IEEE}
}

Introduction

A discriminative structured analysis dictionary is proposed for the classification task. A structure of the union of subspaces (UoS) is integrated into the conventional analysis dictionary learning to enhance the capability of discrimination. A simple classifier is also simultaneously included into the formulated function to ensure a more complete consistent classification. The solution of the algorithm is efficiently obtained by the linearized alternating direction method of multipliers. Moreover, a distributed structured analysis dictionary learning is also presented to address large scale datasets. It can group-(class-) independently train the structured analysis dictionaries by different machines/cores/threads, and therefore avoid a high computational cost. A consensus structured analysis dictionary and a global classifier are jointly learned in the distributed approach to safeguard the discriminative power and the efficiency of classification. Experiments demonstrate that our method achieves a comparable or better performance than the state-of-the-art algorithms in a variety of visual classification tasks. In addition, the training and testing computational complexity are also greatly reduced.

Structured Analysis Dictionary Learning (SADL)

Distributed SADL

Contacts

email: wtang6@ncsu.edu