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README
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***************************************************************************************
***************************************************************************************
Matlab demo code for Gaussian Mixture Model Embedding
This code is for academic purpose only. Not for commercial/industrial activities.
***************************************************************************************
***************************************************************************************
I. NOTICE
=================
The source code includes library for other hashing methods including: Binary Autoencoder (ba),
Iterative Quantization (itq), Spectral Hashing (sh), Spherical Hashing (sph), and K-mean
Hashing (kmh). These libraries are slightly modified but still keeping the algorithm and
recommended parameters from original papers.
II. DATASET
=================
The folder './dataset' contains the mat files used for this demo code. If the './dataset'
folder is empty, please download the dataset from
https://www.mediafire.com/folder/imkwh9v38xr84/Gemb_release and place them in this folder.
+ <dataset>_<feature_type>_train.mat: The files are used for trainning and database.
- train_features (# samples x feature_dim): Extracted features of training images
- train_labels (# samples x 1): Semantic labels of training images
+ <dataset>_<feature_type>_test.mat: The files are used for testing.
- test_features (# samples x feature_dim): Extracted features of testing images
- test_labels (# samples x 1): Semantic labels of testing images
Where:
+ <dataset>: cifar10/mnist/labelme
+ <feature_type>: gist/vggfc7 (for mnist dataset, only gist feature is available)
III. USAGE
=================
Modify the parameters which are defined and explained in 'main_demo.m' properly then run.