cv.BOWImgDescriptorExtractor

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

Such a computation consists of the following steps:

Compute descriptors for a given image and its keypoints set.
Find the nearest visual words from the vocabulary for each keypoint descriptor.
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.Vocabulary = dictionary;
descs = extractor.compute(im, keypoints);

Superclasses	handle
Sealed	false
Construct on load	false

Vocabulary	Visual vocabulary
id	Object ID

	addlistener	Add listener for event.
	compute	Computes an image descriptor using the set visual vocabulary
	compute1	Computes an image descriptor using keypoint descriptors
	compute2	Computes an image descriptor using the set visual vocabulary
	delete	Destructor
	descriptorSize	Returns image discriptor size
	descriptorType	Returns image descriptor type
	eq	== (EQ) Test handle equality.
	findobj	Find objects matching specified conditions.
	findprop	Find property of MATLAB handle object.
	ge	>= (GE) Greater than or equal relation for handles.
	gt	> (GT) Greater than relation for handles.
Sealed	isvalid	Test handle validity.
	le	<= (LE) Less than or equal relation for handles.
	listener	Add listener for event without binding the listener to the source object.
	lt	< (LT) Less than relation for handles.
	ne	~= (NE) Not equal relation for handles.
	notify	Notify listeners of event.