This is basic content Based Image Retrieval project

• split the dataset into train and test sets
• here i used 80 : 20 as the ratio of train and test split

• convert each image in HSV formate
• divide image into 5 segments : top-left, top-right, bottom-right, bottom-left, center as ecllipse
• histogram bins : here 8, 12, 3 bin sizes
• for 1 segment of the image 8*12*3 features
• for 5 segments 5*8*12*3 == 1,440 -- > this is the feature vector size for the single image

• Divide the image into 5 segments
• Finding feature vector by using bins
• Storing the feature vectors in a bag

• For test Image finding feature vector by using bins
• Measure the simmilarity of the image with the features present in our bag