Bag-of-words location retrieval : Including position of local features

Analyzing whether two photos depict the same scene can algorithmically be done by counting the different features of each image and comparing these totals. In doing this, however, information about where in the image each feature was found is discarded. This research investigates possible improvements to using a visual bag-of-words model in automated location retrieval. Two new models for grouping features of an image by their position are proposed and evaluated. Based on the recall rate it is shown that these models can reach a rate of 94%, compared to an 88% rate of the basic bag-of-words implementation. Both models indeed can be applied to improve the performance of bag-of-words based scene recognition. All the code used in this research is available in a public repository at https://github.com/cievers/Location-Retrieval.