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Codes of the paper "Learning Extremely Shared Middle-level Image Representation for Scene Classification"

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# Learning Extremely Shared Middle-level Image Representation for Scene Classification

## Introduction

The implemention codes on MIT-indoor 67 dataset.

A novel method to learn very compact and discriminative image representation.

## Dependencies
The MIT-indoor 67 dataset can be downloaded at [this website](http://web.mit.edu/torralba/www/indoor.html) and should be put under the "data" path.

The [caffe](http://caffe.berkeleyvision.org/) is needed to be installed.

The CNN model can be downloaded at the [caffe model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo) 
and be put under the "feature_extraction_imagenet" path.

[VLFeat](http://www.vlfeat.org/) and [LIBLINEAR](https://www.csie.ntu.edu.tw/~cjlin/liblinear/) are also needed.

## Trained models
We put 4 different trained models "best_2.mat", "best_4.mat", "best_6.mat", "best_8.mat" under the "data" path, 
which correspond to 2 patterns, 4 patterns, 6 patterns, 8 patterns per class respectively.

## Testing
Run the "MITindoor_classification.m" to test the trained model.

## Training
Run the "exp_MITindoorft.m" to train the model.

## License
The code is released under the MIT License.

## Citing paper
If you find the work is useful in your research, please considering citing:

@article{tang2016learning,
        title = {Learning extremely shared middle-level image representation for scene classification},
        author = {Tang, Peng and Zhang, Jin and Wang, Xinggang and Feng, Bin and Roli, Fabio and Liu, Wenyu},
        journal = {Knowledge and Information Systems},
        pages = {1--22},
        year = {2016},
        publisher = {Springer}
    }

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