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https://github.com/dgriffiths3/ml_segmentation
Machine learning semantic segmentation - Random Forest, SVM, GBC
https://github.com/dgriffiths3/ml_segmentation
image-segmentation machine-learning python random-forest segmentation support-vector-machine
Last synced: about 2 months ago
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Machine learning semantic segmentation - Random Forest, SVM, GBC
- Host: GitHub
- URL: https://github.com/dgriffiths3/ml_segmentation
- Owner: dgriffiths3
- Created: 2018-09-03T20:22:52.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2019-05-20T18:11:18.000Z (about 5 years ago)
- Last Synced: 2024-02-08T10:46:36.531Z (5 months ago)
- Topics: image-segmentation, machine-learning, python, random-forest, segmentation, support-vector-machine
- Language: Python
- Homepage:
- Size: 4.95 MB
- Stars: 93
- Watchers: 2
- Forks: 26
- Open Issues: 3
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Metadata Files:
- Readme: README.md
Lists
- awesome-stars - dgriffiths3/ml_segmentation - Machine learning semantic segmentation - Random Forest, SVM, GBC (Python)
README
# Machine Learning - Image Segmentation
Per pixel image segmentation using machine learning algorithms. Programmed using the following libraries: Scikit-Learn, Scikit-Image OpenCV, and Mahotas and ProgressBar. Compatible with Python 2.7+ and 3.X.
### Feature vector
Spectral:
* Red
* Green
* Blue
Texture:
* Local binary pattern
Haralick (Co-occurance matrix) features (Also texture):
* Angular second moment
* Contrast
* Correlation
* Sum of Square: variance
* Inverse difference moment
* Sum average
* Sum variance
* Sum entropy
* Entropy
### Supported Learners
* Support Vector Machine
* Random Forest
* Gradient Boosting Classifier
### Example Usage
python train.py -i -l -c -o
python inference.py -i -m -o
python evaluation.py -i -g [-m]
### Example Output
