Name		Name	Last commit message	Last commit date
parent directory ..
README.md		README.md
coarseness.m		coarseness.m
contrast.m		contrast.m
directionality.m		directionality.m
ex-7-derive-the-tamura-features-for-the-texture-image.pdf		ex-7-derive-the-tamura-features-for-the-texture-image.pdf
getTamuraFeatures.m		getTamuraFeatures.m
learn.mlx		learn.mlx
linelikeness.m		linelikeness.m
output-1.jpg		output-1.jpg
q1.m		q1.m
regularity.m		regularity.m
roughness.m		roughness.m
tamura_image_features.xlsx		tamura_image_features.xlsx

README.md

Implementation of Tamura Features based CBIR System

Date: 25.03.2021

Credits: All the feature calculation function code was referenced from MarshalLeeeee/Tamura-In-Python/referenced-matlab-code/ repository on GitHub

ex-7-derive-the-tamura-features-for-the-texture-image.pdf: Problem Statement for this Lab experiment
coarseness.m: MATLAB function to calculate coarseness for the given grayscale image
contrast.m: MATLAB function to calculate contrast for the given grayscale image
directionality.m: MATLAB function to calculate directionality for the given grayscale image
linelikeliness.m: MATLAB function to calculate linelikeliness for the given grayscale image
regularity.m: MATLAB function to calculate regularity for the given grayscale image
roughness.m: MATLAB function to calculate roughness for the given grayscale image
getTamuraFeatures.m: MATLAB function that returns all the Tamura features for a given image
q1.m: MATLAB code to implement a CBIR using Tamura features and Euclidean distance metric
tamura_image_features.xlsx: Generated XLSX file containing the Tamura feature values extracted for a set of images