author = {Tom Botterill and Steven Mills and Richard Green},
title = {{Speeded-up Bag-of-Words algorithm for robot localisation through
scene recognition}},
booktitle = {Image and Vision Computing New Zealand},
year = {2008},
pages = {1-6},
month = {November},
abstract = {This paper describes a new scalable scheme for the real-time detection
of identical scenes for mobile robot localisation, allowing fast
retraining to learn new environments. It uses the Image Bag-of-Words
algorithm, where images are described by a set of local feature descriptors
mapped to a discrete set of ‘image words’. This scheme uses descriptors
consisting of a combination of a descriptor of shape (SURF) and a
hue histogram, and this combination is shown to perform better than
either descriptor alone. K-medoids clustering is shown to be suitable
for quantising these composite descriptors (or any arbitrary descriptor)
into visual words. The scheme can identify in real-time (0.036 seconds
per query) multiple images of the same object from a standard dataset
of 10200 images, showing robustness to differences in perspective
and changes in the scene, and can detect loops in a video stream
from a mobile robot.},
doi = {10.1109/IVCNZ.2008.4762067}