Improvement of bag of visual words using Iconclass

Recently, bag-of-visual-words has been paid attention to as an image retrieval approach that uses the defining features of images. However, k-means clustering generally used in bag-of-visual-words has a drawback such that its result is affected by setting up initial points and their number. Additionally, the more keypoints increase, the more expensive processing becomes. We resolve the problem of bag-of-visual-words by using a quantizing method that we have developed. In addition, we have developed a theme comprehending system that uses ontology.