For each dataset we used the first half of the images as training data on which we performed the *k-means* clustering and generated a vocabulary of $5000$ visual words. We used $128$-dimensional SIFT feature descriptors as the input to the clustering. Each image has $\sim 400$ SIFT descriptors on average.

The other half of the sequences have been used as the test data, i.e., query images. Each query is matched to the earlier images in the sequence. For each query there are multiple matches in ground truth. If the match that we find for each query is among those correct(ground truth) matches, we call the match correct otherwise incorrect.

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   ![Images from the New College (top row) and City Center (bottom row) sequences used in the reported experiment.[]{data-label="fig:datasets"}](./images/college_0007.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/college_0442.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/college_1732.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_0301.jpg "fig:"){width=".15\textwidth"}      ![Images from the New College (top row) and City Center (bottom row) 