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Bag-of-words model in computer vision : ウィキペディア英語版 | Bag-of-words model in computer vision In computer vision, the bag-of-words model (BoW model) can be applied to image classification, by treating image features as words. In document classification, a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary. In computer vision, a ''bag of visual words'' is a vector of occurrence counts of a vocabulary of local image features. ==Representation based on the BoW model==
抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)』 ■ウィキペディアで「Bag-of-words model in computer vision」の詳細全文を読む
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