Unsupervised and Fast Continent Classification of Digital Image Collections using Time
Original version
Sandnes, F.E. (2010). Unsupervised and Fast Continent Classification of Digital Image Collections using Time. In: W. Wang (Ed.), Proceedings of ICSSE 2010. Piscataway, N.J. : Institute of Electrical and Electronics Engineers http://dx.doi.org/10.1109/ICSSE.2010.5551803Abstract
Advances in storage capacity means that digital
cameras can store huge collections of digital photographs.
Typically such images are given non-descriptive filenames names
such as a unique identifier, often an integer. Consequently it is
time-consuming and difficult to browse and retrieve images from
large collections especially on small consumer electronics devices.
A strategy for classifying images into geographical regions is
presented which allows images to be coarsely sorted into the
continent where they were taken. The strategy employs patterns
in the time-stamps of images to identify events such as holiday
and individual days, and to estimate the approximate longitude
where the photographs were taken. Experimental evaluations
demonstrate that the continent is correctly estimated for 89 % of
the images in arbitrary collections and that the longitude is
estimated with a mean error of 27.5 degrees. The strategy is
relatively straightforward to implement, also in hardware, and
computationally inexpensive.