Phase 1: In this project, I have experimented with : • image features, • vector models, and • similarity/distance measures INSTALLATION: Requirements: • Python 3.8+ • Windows/Ubuntu • Anaconda • JupyterLab/JupyterNotebook -- Installing ANACONDA- Please install latest version for your system from https://www.anaconda.com/products/individual. -- Installing Python on CONDA environment conda install -c anaconda python -- Install JupyterLab on CONDA conda install -c conda-forge jupyterlab -- Installing required Libraries: -OpenCV - conda install -c conda-forge opencv -Numpy - pip install numpy -Glob - pip3 install glob3 -PIL - python3 -m pip install --upgrade pip python3 -m pip install --upgrade Pillow -Skimage - pip install scikit-image -Scipy - git clone https://github.com/scipy/scipy.git scipy -Natsort - pip install natsort [Important] Additional Libraries might be required. PLease check requirements.txt for more detailed view. USAGE: The code is written on a JupyterLab environment. Each code blocks can be run individually. The code is divided into Task 0, Task 1-2 and Task 3-4. First I have calculated image features: 1. Color Moments 2. Extended Local Binary Patterns 3. Histogram Of Oriented Gradients All the sample output are stored in the outputs folder and the results has been explained in report. There are two text files which contains output for task 3 and task 4. Seperately a .csv file has been made where all the features for each image in the dataset have been saved. The code is well commented and should be self explanatory. The code(.ipynb file) is contained in the code folder Thank You!