BagOfVisualWords

Image categorization using Bag of visual words approach.

Uses opencv python-contrib SIFT for feature extraction and scikit-learn's SVM.

This is python implementation of Bag of visual words model, which again is based on the paper by Csurka et al[1].

Please refer here, for details regarding implementation of the model.

Things you need to install before running:

pickle
scikit-learn
opencv-contrib (it contains sift implementation)
numpy
matplotlib

Project architecture:

  -BagOfVisualWords/
        |- images/
                  |-train/
                          |-category 1
                          |-category 2
                          |-etc
                  |-test/
                          |-category 1
                          |-category 2
                          |-etc
                  |-kmeans/
                          |-category 1
                          |-category 2
                          |-etc
        |- bag_of_words.py
        |- read_files.py
        |- plot_data.py
        |- all other python and sav files.

Things to remember before running:

Change the path to images in the read_files.py file, to point to the directory containing test, train, kmeans images.
Change the path in the KMeans_clustering.py to point to the directory containing kmeans images.

Usage:

python bag_of_words.py

Additional Information:

I have also included the cluster center files, which were obtained using KMeans_clustering from the sift features of 1000 images.
They are named as bov_pickle_Numberofclustercentres.py. (Number of clusters being 200, 400, 600, 800). Feel free to use them.
All the data required for testing, training and also for vocabulary building has been collected from mostly Caltech101 and few images from Caltech256.

Results:

Number of categories	Accuracy	No of clusters used
3	83%	600
3	85%	800
4	76%	600
4	76%	800
5	81%	600
5	80%	800
6	67%	600
6	67%	600

Please check the results folder for the confusion matrices obtained. ex: confusion matrix of 5 categories with 600 clusters.

Documentation:

Each python file contains the information about what each function does, what arguements each fucntion takes and how they can be tweaked.

References:

(1)[https://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/csurka-eccv-04.pdf].

Feel free to open an Issue if you face any problem. :)

ymdatta/BagOfVisualWords