Bag-of-words model in computer vision
Image classification model / From Wikipedia, the free encyclopedia
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In computer vision, the bag-of-words model (BoW model) sometimes called bag-of-visual-words model [1][2] can be applied to image classification or retrieval, 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.