\[fig:feat\_im\] shows another experiment that validates our proposed stopping method. Figure shows the relation between the number of images and the percentage of features they need to process to obtain the same result as that of the original BoW. The experiment has been done on City Center dataset (left-side sequence) with the stopping rule \#1 used. We set the threshold to $0.29$ to get exactly the same accuracy of original BoW. As can be seen, only $338$ images need to process $90\%$$100\%$ of their features, another $223$ only need to vector-quantize smaller percentages of features.

CONCLUSION {#sec:conclud}
==========

We observed that the computational requirements of the BoW method can be significantly reduced by allocating less computations to easier search queries. Deciding when to terminate the search is treated as a stopping problem. We proposed several stopping rules and showed their effectiveness on an appearance-based localization problem.

[99]{}

S. Achar, C. V. Jawahar and K. Madhava Krishna, “Large scale visual localization in urban environments”, in [*Proc. of the IEEE International Conference on Robotics and Automation (ICRA)*]{}, 2011.

A. Angeli, D. Filliat, S. Doncieux, and J.-A. Meyer, “A fast and incremental method for loop-closure detection using bags of visual words”, [*IEEE Transactions 