Automatic Removal of Visual Stop-Words

This paper presents a new methodology for the automatic estimation of the optimal amount of visual words that can be removed from a visual dictionary, such that no harm is induced in the discriminative potential of the resulting bag-of-visual-words representations. The proposed approach relies on a special definition of the entropy of each visual word when considered as a random variable, and a new definition of the overlap of class models computed with a normalized Bhattacharyya coefficient. We combined our proposed methodology with a recent approach that labels visual words as stop-words showing that this combination is beneficial to reduce the dimensionality of bag representations, while obtaining good results in terms of classification accuracy and retrieval performance.