Howtosignalprocessing
- How visual bag of words features are extracted?
- How does bag of visual words work?
- Which phase in bag of features framework generates visual words?
- How do we use clustering to compute a bag of words image representation?
How visual bag of words features are extracted?
Its concept is adapted from information retrieval and NLP's bag of words (BOW). In bag of words (BOW), we count the number of each word appears in a document, use the frequency of each word to know the keywords of the document, and make a frequency histogram from it. We treat a document as a bag of words (BOW).
How does bag of visual words work?
The bagOfFeatures object defines the features, or visual words, by using the k-means clustering (Statistics and Machine Learning Toolbox) algorithm on the feature descriptors extracted from trainingSets . The algorithm iteratively groups the descriptors into k mutually exclusive clusters.
Which phase in bag of features framework generates visual words?
After finding the optimum keypoints from the new GKS method, the codebook construction phase of BOF (as described in “Bag-of-features method” section) is performed which uses K-means clustering to generate various visual words.
How do we use clustering to compute a bag of words image representation?
The number of the clusters is the codebook size (analogous to the size of the word dictionary). Thus, each patch in an image is mapped to a certain codeword through the clustering process and the image can be represented by the histogram of the codewords.
