Class to compute an image descriptor using the bag of visual words

Such a computation consists of the following steps:

1. Compute descriptors for a given image and its keypoints set.
2. Find the nearest visual words from the vocabulary for each keypoint descriptor.
3. Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words encountered in the image. The i-th bin of the histogram is a frequency of i-th word of the vocabulary in the given image.

Example

% create bag of visual words
trainer = cv.BOWKMeansTrainer(K);
dictionary = trainer.cluster(train_descs);

% Compute histogram of visual word occurrences of an image
extractor = cv.BOWImgDescriptorExtractor('SIFT','BruteForce');
extractor.setVocabulary(dictionary);
descs = extractor.compute(im, keypoints);