This paper presents a new framework for visual bag-of-words (BOW) refinement and
reduction to overcome the drawbacks associated with the visual BOW model which has …

This paper presents a novel semantic regularized matrix factorization method for learning
descriptive visual bag-of-words (BOW) representation. Although very influential in image …

The bag of visual words (BOW) model has been widely applied in the field of image
recognition and image classification. However, all scale‐invariant feature transform (SIFT) …

The Bag-of-Words (BoW) model is a promising image representation technique for image
categorization and annotation tasks. One critical limitation of existing BoW models is that …

In the image classification field, the visual bag of words (BoW) has two drawbacks. One is
low classification accuracy because a visual BoW is typically extracted from local low-level …

Image analysis tasks such as classification, clustering, detection, and retrieval are only as
good as the feature representation of the images they use. Much research in computer …

Bag-of-visual-words has been shown to be a powerful image representation and attained
success in many computer vision and pattern recognition applications. Usually for a given …

In recent years researchers have worked to understand image contents in computer vision.
In particular, the bag of visual words (BoVW) model, which describes images in terms of a …

Bag of visual words model has recently attracted much attention from computer vision
society because of its notable success in analysing images and exploring their content. This …

In the problem where there is not enough data to use Deep Learning, Bag-of-Visual-Words
(BoVW) is still a good alternative for image classification. In BoVW model, many pooling …