------------- -------- -------- -------- --------

  : Comparison of our approach to the original BoW on the New College dataset, left-side sequence, 1395 words.[]{data-label="exp1:newcol2"}

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4.5pt

  -- --------- ------------- -------- -------- -------- --------
                    BoW                                 
                (Inv. Indx)                             
      top 3:      0.9487      0.9397   0.9299   0.8778   0.8150
      top 5:      0.9780      0.9707   0.9544   0.9356   0.8818
      top 10:     0.9902      0.9894   0.9804   0.9658   0.9487
         1        0.8567      0.7374   0.5517   0.3211  
         -         0.25        0.20     0.15     0.10   
  -- --------- ------------- -------- -------- -------- --------

  : Comparison of our approach to the original BoW on the New College dataset, left-side sequence, 1395 words.[]{data-label="exp1:newcol2"}

Different Stopping Rules {#sec:rules}
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   ![Accuracy (recall) vs. % of features used, City Center dataset[]{data-label="fig:rules2"}](./images/feat_Acc_3rules_top5_im.eps "fig:"){width=".40\textwidth"}
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   ![Accuracy (recall) vs. % of features used, City Center dataset[]{data-label="fig:rules2"}](./images/feat_Acc_city_odd.eps "fig:"){width=".40\textwidth"}
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We used different stopping rules in our experiments and compared the efficiency of each of them when applied to the localization problem. Three rules have been used that are explained below:

-   Rule 1: computes the distance between the peak and the average of the similarity histogram (i.e., the histogram that shows the similarity of database images to the query). Once the distance is above some threshold, it stops the search: Stop if $|\max(hist)-\mbox{mean}(hist)| > 