Abstract
Traditional program guides, TV applications, and online portals alone are no longer sufficient to expose all content, let alone offer the content that consumers want, at the times they are most likely to want it. DEEP, (Data Enrichment and Engagement Platform) by Orca Interactive, a comprehensive new content discovery solution, combines search, recommendation, and second-screen devices into a single immersive experience which invites exploration. The automated generation (using internet sources) of digital magazines for movies, TV shows, cast members and topics is a key value of DEEP. Unfortunately, using the internet as a source for pictures can result in the acquisition of so-called “Near Duplicate” (ND) images – similar images from a specific display context - for example, multiple red carpet images showing an actor from very similar angles or degrees of zoom on him/her. Therefore, in this paper we present a practical application of ND detection for image databases. The algorithm used is based on the MPEG-7 Colour Structure descriptor. For images that were provided by the developers of the DEEP software the algorithm performs very well, and the results are almost identical to those obtained during the training phase.
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Eshkol, A., Grega, M., Leszczuk, M., Weintraub, O. (2014). Practical Application of Near Duplicate Detection for Image Database. In: Dziech, A., Czyżewski, A. (eds) Multimedia Communications, Services and Security. MCSS 2014. Communications in Computer and Information Science, vol 429. Springer, Cham. https://doi.org/10.1007/978-3-319-07569-3_6
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DOI: https://doi.org/10.1007/978-3-319-07569-3_6
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