Summarised Presentation of Personal Photo Sets. In: Gurrin, Cathal; Hopfgartner, Frank; Hurst, Wolfgang; Johansen, Håvard; Lee, Hyowon; O’Connor, Noel (Ed.): MultiMedia Modeling, vol. 8325, pp. 195-206, Springer International Publishing, 2014, ISBN: 978-3-319-04113-1.
Abstract
People produce an increasing amount of digital photos to document events. Searching for a specific event can result in more photos than people can handle, making difficult judging their relevance. This paper presents a new algorithm, that summarises a set of photos described by attributes at different concept levels. It addresses the well-known human weakness to deal with large collections of distinct items, by presenting a low cardinality partition set. Each group yields a compact, yet distinct, description. The evaluation, including user tests, shows the algorithm outperforms others in context separation and informative power about the set being summarised.
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@incollection{Datia2014,
title = {Summarised Presentation of Personal Photo Sets},
author = { Nuno Datia and João Moura Pires and Nuno Correia},
editor = {Gurrin, Cathal and Hopfgartner, Frank and Hurst, Wolfgang and Johansen, Håvard and Lee, Hyowon and O’Connor, Noel},
url = {http://dx.doi.org/10.1007/978-3-319-04114-8_17},
isbn = {978-3-319-04113-1},
year = {2014},
date = {2014-01-01},
booktitle = {MultiMedia Modeling},
volume = {8325},
pages = {195-206},
publisher = {Springer International Publishing},
series = {Lecture Notes in Computer Science},
abstract = {People produce an increasing amount of digital photos to document events. Searching for a specific event can result in more photos than people can handle, making difficult judging their relevance. This paper presents a new algorithm, that summarises a set of photos described by attributes at different concept levels. It addresses the well-known human weakness to deal with large collections of distinct items, by presenting a low cardinality partition set. Each group yields a compact, yet distinct, description. The evaluation, including user tests, shows the algorithm outperforms others in context separation and informative power about the set being summarised.},
keywords = {Attribute induction, Clustering, Human factors, Multimedia Summarisation},
pubstate = {published},
tppubtype = {incollection}
}