Demo code of our TIP 2016 paper "Correlated Logistic Model with Elastic Net Regularization for Multilabel Image Classification"
%% Code authors: Wei Bian and Qiang Li
%% Release time: Nov. 11th, 2016
%% Current version: CorrLog_v1
- Run the Main code.
Note the difference between 'script_CV.m' and 'script_TT.m',
the first corresponds to cross-validation based experiments,
the second corresponds to train/test based experiments.
The datasets and results are in 'data/' folder.
- Datasets and Feature normalization.
We only used MULANscene in this demo. For other datasets, please follow the guidance in our TIP paper.
For MULANscene and XXXX-PHOW, better to use "whitening" normalization.
For XXXX-CNN, better to apply no normalization.
- Two methods are implemented.
CorrLog.m, corresponds to the previous model using L2 regularization.
enCorrLog.m, corresponds the updated model using elastic net regularization.
%% Reference noticement:
If you have used the code, please cite both of the two papers:
[1] Qiang Li, Bo Xie, Jane You, Wei Bian, and Dacheng Tao,
"Correlated Logistic Model with Elastic Net Regularization for Multilabel Image Classification,"
IEEE Trans. on Image Processing (T-IP), vol.25, no.8, pp.3801-3813, 2016.
[2] Wei Bian, Bo Xie, and Dacheng Tao,
"CorrLog: Correlated Logistic Models for Joint Prediction of Multiple Labels,"
in Proc. Int. Conf. Artif. Intell. Stat. (AISTATS), 2012, pp.109¨C117.
%% Supporting information:
If any questions and comments, feel free to send your email to
Qiang Li (leetsiang.cloud@gmail.com)