sequences used in the reported experiment.[]{data-label="fig:datasets"}](./images/city_1840.jpg "fig:"){width=".15\textwidth"}      ![Images from the New College (top row) and City Center (bottom row) sequences used in the reported experiment.[]{data-label="fig:datasets"}](./images/city_0048.jpg "fig:"){width=".15\textwidth"}
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Performance Evaluations
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On each dataset we ran the BoW with the original voting scheme (inverted-index) and BoW with our voting scheme that employs stopping rules. Each visual word in the query image has been weighed with the *tf-idf* score and the top-$n$ images with highest scores have been returned as match candidates. We set $n$ to 3, 5 and 10 in our experiments. We select the candidates whose similarity to the query is above some threshold. Our experimental results have been summarized in Table \[exp1:city1\] to Table \[exp1:newcol2\]. The reported recall values are for precisions above 90%. Top 3, top 5 and top 10 in Tables \[exp1:city1\] to \[exp1:newcol2\] indicate the top-3, -5 and -10 nearest neighbors to the query image that we retrieved. We used different stopping thresholds to generate different recalls. The percentage of the features that we processed and the accuracy of the localization have been computed and compared with the original BoW. All the results have been produced by averaging over $10$ Monte Carlo runs. The 