• Build a Classifier to classify metropolitian & countryside
• Using:
  • K-Means clustering, histogram (HOG) for prerocess data and extract features
  • LogisticsRegression for classify metropolitian and countryside
• Data: metropolitian & countryside

• Preprocess Images : Using opencv for imread data with function load_image and resize output image (350,350)

• K-Means Clustering : We will extract dominant colors with k = 5 centroid with function setUPKmean

• HOG (Histogram Oriented Gradients) : Construct the features vector for each image using 5 clustered RGB colors, sorted by color frequency.

• LogisticRegression : We use Logistic Regression model to classify the feature vectors built from the HOG. Implement the Gradient Descent method to optimize the model.

   batch_size = 32 epochs = 10 threshold = 0.5 learning_rate = 0.001 

• Accuracy: 73.8 % with 1206 samples ( validation set = 20% , training set = 80%)

• Output K-means Clustering with centroid = 5:

  

• Vector Histogram:

  

• Ouput HOG:

  

 Classification/ model/ model.sav # model results after training resources_data/ countryside # contains data of countryside ( have some image for intuition) metropolitian # contains data of metropilitian ( have some image for intuition) image_predict # contains image for test predict output_image_test # some results of k-mean and hog predict.py # predict some image from resources_data/image_predict with args pathto model preprocess_image.py # contains imread data, fit k-mean and hog setup_model.py # Build LogisicRegression from scatch train.py # start training with args path data train 

 skimage opencv-python numpy matplotlib sklearn 

python train.py --inputcountry path_to_countrydata/ --inputmetro path_to_metropolitiandata/

python predict.py --model path_to_model/ --image path_to_image/