Implementation of a content based image classifier using the bag of visual words model in Python.

Weak Features, M = 2500, Single, Level 0 (1x1),Linear SVM(C=100.0)

Strong Feature, M = 600, Single, Level 0 (1x1),Linear SVM(C=200.0 and 250.0)

Google Colab link : https://colab.research.google.com/drive/1_URYuLjPFqQGQ_-bWpwYIAV1NdV9rPl6

Google Drive Link for Download .pkl files

Click Google Colab Link -> Runtime type change (GPU) -> Variable run_all_process = True -> Run all

1. Prepare Dataset : Caltech-101 Dataset

2. feature extraction : SIFT descriptors - Opencv Version(3.4.2.16) Downgrade for SIFT Features

3. clustering and build codebook : K-means clustering algorithm 

4. Image representation(making the histogram of features) : Vector Quantization 

5. classifier learning and recognition : SVM Classifier

BoW Process:https://github.com/CyrusChiu/Image-recognition

K-Means Clustering using GPU : https://github.com/ilyaraz/pytorch_kmeans

Multi-class Linear SVM using GPU : https://github.com/murtazajafferji/svm-gpu

Porting by glee1228@naver.com