The state-of-the-art algorithms for large visual content recog- nition and content based similarity search today use the Bag of Features (BoF) or \Bag of Words (BoW) approach. The idea, borrowed from text retrieval, enables the use of inverted files. A very well known issue with the BoF approach is that the query images, as well as the stored data, are described with thousands of words. This poses obvious efficiency problems when using inverted files to perform efficient image matching. In this paper, we propose and compare various techniques to reduce the number of words describing an image to improve efficiency.
Amato, G., Falchi, F., & Gennaro, C. (2013). Visual features selection. In CEUR Workshop Proceedings (Vol. 964, pp. 41–44). CEUR-WS. https://doi.org/10.3917/jdp.319.0072