Background

Bag of words models are a popular technique for image classification inspired by models used in natural language processing. The model ignores or downplays word arrangement (spatial information in the image) and classifies based only on a histogram of the frequency of visual words. Visual words are identified by clustering a large corpus of example features.

Algorithm

The basic flow of the algorithm:

Collect a lot of features.

Use k-means to cluster those features into a visual vocabulary.

For each of the training images build a histogram of the word frequency (assigning each feature found in the training image to the nearest word in the vocabulary).

Feed these histograms to an SVM.

Build a histogram for test images and classify them with the SVM you just trained.

Results

Confusion matrix which displaying the accuracy of the classifier