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List item #3 from the above link reads:
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.
The BOWImageDescriptorExtractor.compute() method returns a normalized histogram of the occurrences of each vocabulary word.
This means that if there are 10 words in the image, with 5 occurrences of word 0 and 5 occurrences of word 2, then the 0th element in the histogram would possess 5/10, or .5, as well as the element at index 2. All other elements would hold a value of zero.
