features to find the closest match to the query (indicated by the peak of the histogram), however the easy search can stop the search after processing only $12\%$ of features as the peak does not change until the end of the search. Comparing the distance between the peak and average of the histograms, it can be seen that in difficult search this distance is smaller than that in easy search, which is expected. Initially each image starts with no vote. In original BoW, image features are converted to visual words one by one and the histogram (of images’ scores) is built incrementally. Each bin of the histogram corresponds to one of the database images and indicates the score of that image. Each visual word will cast a distance-weighted vote for multiple images, i.e., histogram bins. This process continues until all words cast their votes and then the peak of the histogram (the bin with the highest score) determines the nearest neighbor to the query.[^4]

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                           ![Easy search. The Query image (a) has been matched to (b). (c) shows the histogram of images’ scores after vector-quantizing 12% of features. The peak of the histogram (shown by the red arrow) does 