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Semantic content filtering using self-organizing neural networks

Semantic content filtering using self-organizing neural networks

Daylamani-Zad, Damon (2007) Semantic content filtering using self-organizing neural networks. In: 2nd International Workshop on Semantic Media Adaptation and Personalization, 17-18 December 2007, Uxbridge, UK.

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Abstract

OSMOS-7 is an application that can create and filter MPEG-7 semantic content models with regards to objects and events, both spatially and temporally. The results are presented as numerous video segments that are all relevant to the user's consumption criteria, yet these results are not ranked according to the user's preferences. Using self organizing networks (SONNs) we rank the segments to the user's preferences by applying the knowledge gained from similar users' experience and use content similarity for new segments to derive a relative ranking. To bridge the gap between the user preferences and the content model, an MPEG- 7 model is proposed that uses the hanging basket model to better relate the users 'preferences and usage history to the content.

Item Type: Conference or Conference Paper (Paper)
Uncontrolled Keywords: Self organizing neural networks, Multimedia MPEG-7, Users preferences hanging basket model, Content Modelling
Pre-2014 Departments: School of Computing & Mathematical Sciences > Department of Computing and Information Systems
Last Modified: 14 Oct 2016 09:28
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/12169

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