Histogram_Layer

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Histogram_Layer

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Histogram layer code for "Histogram Layers for Texture Analysis" (https://arxiv.org/abs/2001.00215).

Histogram_Layer 최신버젼 다운로드

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# Histogram Layer:
**Histogram Layers for Texture Analysis**

_Joshua Peeples, Weihuang Xu, and Alina Zare_

Note: If this code is used, cite it: Joshua Peeples, Weihuang Xu, & Alina Zare. 
(2020, March 25). GatorSense/Histogram_Layer: Initial Release (Version v2.0). 
Zenodo. https://doi.org/10.5281/zenodo.3731417
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3731417.svg)](https://doi.org/10.5281/zenodo.3731417)

[[`IEEE Transactions on AI`](https://ieeexplore.ieee.org/document/9652037)]

[[`arXiv`](https://arxiv.org/abs/2001.00215)]

[[`BibTeX`](#CitingHist)]

In this repository, we provide the paper and code for histogram layer models from "Histogram Layers for Texture Analysis."

## Installation Prerequisites

This code uses python, pytorch, and barbar. 
Please use [[`Pytorch's website`](https://pytorch.org/get-started/locally/)] to download necessary packages.
Barbar is used to show the progress of model. Please follow the instructions [[`here`](https://github.com/yusugomori/barbar)]
to download the module.

## Demo

Run `demo.py` in Python IDE (e.g., Spyder) or command line. To evaluate performance,
run `View_Results.py` (if results are saved out).

## Main Functions

The histogram layer model (HistRes_B) runs using the following functions.

1. Intialize model

```model, input_size = intialize_model(**Parameters)```

2. Prepare dataset(s) for model

```dataloaders_dict = Prepare_Dataloaders(**Parameters)```

3. Train model

```train_dict = train_model(**Parameters)```

4. Test model

```test_dict = test_model(**Parameters)```

## Parameters
The parameters can be set in the following script:

```Demo_Parameters.py```

## Inventory

```
https://github.com/GatorSense/Histogram_Layer

└── root dir
    ├── demo.py   //Run this. Main demo file.
    ├── Demo_Parameters.py // Parameters file for demo.
    ├── Prepare_Data.py  // Load data for demo file.
    ├── Prepare_Data_Results.py // Load data for results file.
    ├── Texture_Information.py // Class names and directories for datasets.
    ├── View_Results.py // Run this after demo to view saved results.
    ├── papers  // Links to related publications.
    └── Utils  //utility functions
        ├── Compute_FDR.py  // Compute Fisher Discriminant Ratio for features.
        ├── Confusion_mats.py  // Generate confusion matrices.
        ├── Generate_TSNE_visual.py  // Generate TSNE visualization for features.
        ├── Histogram_Model.py  // Generate HistRes_B models.
        ├── Network_functions.py  // Contains functions to initialize, train, and test model. 
        ├── RBFHistogramPooling.py  // Create histogram layer. 
        ├── Save_Results.py  // Save results from demo script.
     
```

## License

This source code is licensed under the license found in the [`LICENSE`](LICENSE) file in the root directory of this source tree.

## <a name="CitingHist"></a>Citing Histogram Layer

If you use the histogram layer code, please cite the following reference using the following entry.

**Plain Text:**

Peeples, J., Xu, W., & Zare, A. (2021). "Histogram Layers for Texture Analysis," 
in IEEE Transactions on Artificial Intelligence, DOI 10.1109/TAI.2021.3135804.

**BibTex:**
```
@Article{Peeples2021Histogram,
Title = {Histogram Layers for Texture Analysis},
Author = {Peeples, Joshua and Xu, Weihuang  and Zare, Alina},
Journal = {IEEE Transactions on Artificial Intelligence},
Volume = {},
Year = {2021},
number={}
pages={1-1}
doi={10.1109/TAI.2021.3135804}
}
```

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