T$ and return the image corresponding to the peak.\

-   Rule 2: computes the relative distance between the peak and the average of the histogram. Once the distance is above some threshold it stops the search: Stop if $|\max(h)-\mbox{mean}(h)|/\mbox{mean}(h) > T$ and return the image corresponding to the peak.\

-   Rule 3: keeps track of the peak of the histogram. If the peak does not change after processing $T$ features, the image corresponding to the peak is returned as the nearest neighbor to query.

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   ![Our experiment on City Center dataset with our stopping method that shows only $60\%$ of images need to process $90\%$$100\%$ of their features to get the accuracy of BoW.[]{data-label="fig:feat_im"}](./images/feat_im3.eps "fig:"){width=".35\textwidth"}     
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The experimental results shown in Tables \[exp1:city1\] to \[exp1:newcol2\] have been performed by Rule 1. Figures \[fig:rules\] and \[fig:rules2\] show the result of applying different rules to the BoW search on the New College (right-side sequence) and City Center (right-side sequence) datasets, respectively. The graphs have been generated by varying the stopping thresholds. As can be seen, stopping rules \#1 and \#3 generate better results. By reducing the number of processed features to half the accuracy only decreases slightly.

Figure 