Recently, bag-of-visual-words has been studied 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 in that its results are affected by setting initial points and their number. Additionally, the more the number of keypoints increases, the more expensive processing becomes. We solve these problems 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.