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https://github.com/vdevmcitylp/local-descriptors-for-image-classification
Local Descriptors
https://github.com/vdevmcitylp/local-descriptors-for-image-classification
descriptors image-processing local-binary-patterns local-ternary-patterns
Last synced: about 2 months ago
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Local Descriptors
- Host: GitHub
- URL: https://github.com/vdevmcitylp/local-descriptors-for-image-classification
- Owner: vdevmcitylp
- Created: 2017-12-15T17:20:33.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2021-05-19T14:11:25.000Z (over 3 years ago)
- Last Synced: 2024-08-04T03:12:08.048Z (5 months ago)
- Topics: descriptors, image-processing, local-binary-patterns, local-ternary-patterns
- Language: Python
- Homepage: https://vdevmcitylp.github.io/local-descriptors-for-image-classification/
- Size: 29.3 KB
- Stars: 22
- Watchers: 2
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# local-descriptors-for-image-classification
UPDATE: My paper has been accepted! Check it out [here](https://www.igi-global.com/article/center-symmetric-local-descriptors-for-image-classification/217023).
Each file implements one of the variants of the [Local Binary Pattern (LBP)](http://jultika.oulu.fi/files/isbn9514270762.pdf).
1. [Center Symmetric LBP](http://www.ee.oulu.fi/mvg/files/pdf/pdf_750.pdf)
2. [Center Symmetric Local Derivative Pattern](https://ieeexplore.ieee.org/document/6011859/)
3. Center Symmetric Local Derivative Mapped Pattern
4. [Center Symmetric Local Mapped Pattern](https://dl.acm.org/citation.cfm?id=2554895)
5. [Center Symmetric Local Ternary Pattern](https://www.computer.org/csdl/proceedings/cvpr/2010/6984/00/05540195-abs.html)
6. [Extended Center Symmetric Local Binary Pattern](https://hal.archives-ouvertes.fr/hal-01227955/document)
7. Extended Center Symmetric Local Mapped Pattern
8. [Extended Center Symmetric Local Ternary Pattern](https://link.springer.com/chapter/10.1007/978-3-642-23321-0_56)
## Setup
### Environment
```
- Python 3.7.6
- Ubuntu 16.04
```
Preferably, create a new environment using [virtualenv](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
### Requirements
After activating the virtual environment, run the following command to install dependencies.
```
pip install -r requirements.txt
```
### Datset Setup
Download the CIFAR-10 dataset from [here]() and extract in the `dataset` directory.
For CIFAR-10, run this command to convert the images to grayscale.
```
python data_helper.py
```
This is a helper function for loading, preprocessing and saving the preprocessed images to disk for the CIFAR-10 dataset
Please write your own function for a custom dataset which,
- Converts images to grayscale (you can use OpenCV functions)
- Saves the converted images to disk
All the files in ```operators/``` have the same underlying structure with the only difference being in the algorithm being implemented.
All algorithms are trained and tested on the CIFAR-10 dataset.
## To compute features
python main.py --operator cslbp --img_height 32 --img_width 32
The above command will compute CS-LBP features, check the `operator` argument for more choices.
## Brief Explanation
### Converting to Grayscale
These local descriptors require the input to be in grayscale.
grayscaleImg = (imRed*0.3 + imGreen*0.59 + imBlue*0.11).astype(int)
### The Algorithm
First, I pad the image with zeros before running the algorithm.
img = np.concatenate((img, zeroVertical), axis=1)
img = np.concatenate((zeroVertical, img), axis=1)
img = np.concatenate((zeroHorizontal, img), axis=0)
img = np.concatenate((img, zeroHorizontal), axis=0)
The function then goes on to implement the respective algorithm (CS-LBP in this case).
pattern_img = np.zeros((img_height + 1, img_width + 1))
for x in range(1, img_height + 1):
for y in range(1, img_width + 1):
s1 = threshold(img[x-1, y-1] - img[x+1, y+1])
s2 = threshold(img[x-1, y] - img[x+1, y])*2
s3 = threshold(img[x-1, y+1] - img[x+1, y-1])*4
s4 = threshold(img[x, y+1] - img[x, y-1])*8
s = s1 + s2 + s3 + s4
pattern_img[x, y] = s
We then compute the histogram of the resultant image to get the feature vector.
histogram = np.histogram(pattern_img, bins = np.arange(17))[0]
### Classification
python classification.py --operator cslbp
I'm using the [XGBoost](https://xgboost.readthedocs.io/en/latest/) Classifier for classification.
model = XGBClassifier(n_estimators=800)
model.fit(X_train, y_train)
You have to play with *n_estimators* to get the best accuracy.
And that's about it! Feel free to open an issue if required.