arasharchor / image-classification

texture image classification with python

image-classification

Texture image classification with Python and MATLAB

Image source: http://www.nada.kth.se/cvap/databases/kth-tips/download.html (use link 'greyscale PNG Images' - 23MB)

Textures Images used: Aluminium Foil, Corduroy, and Orange Peel.

Texture Images

Train set: 120 images (40 images from each class)

Test set: 120 images (40 images from each class)

Features (extracted using Matlab):

1. Gray-level co-occurrence matrix (GLCM): Energy and Entropy.
2. Fast Fourier Transform (FFT): Mean and Variances.

Classification method:

1. K-nearest neighbor
2. Gaussian Naïve-bayes

Evaluation: classification accuracy

The Recipes

Extracting Features with Matlab

1. Download texture image dataset
2. Collect in one folder, rename images3.
3. Run .m file
4. Save dataku.mat file (don't worry! dataku.mat file is provided here). To know more about the detail, I am preparing to upload the MATLAB code later.

Number of features: 4:

1. attribute 1: Entropy of GLCM
2. attribute 2: Energy of GLCM
3. attribute 3: Mean of FFT
4. attribute 4: Variance of FFT

Classifying the images

KNN

python imageclassification3_knn.py 

Note: You can change line 14 to switch the features of GLCM, FFT, or all features.

GNB

python imageclassification4_gnb.py 

Note: You can change line 14 to switch the features of GLCM, FFT, or all features.

Result

KNN with GLCM, FFT

GNB with GLCM, FFT

KNN with GLCM + FFT and GNB with GLCM + FFT