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Automatic Cartoon Image Re-authoring Using SOFM.

Authors :
Gunsel, Bilge
Jain, Anil K.
Tekalp, A. Murat
Sankur, Bülent
Han, Eunjung
Park, Anjin
Jung, Keechul
Source :
Multimedia Content Representation, Classification & Security; 2006, p403-409, 7p
Publication Year :
2006

Abstract

According to the growth of the mobile industry, a lot of on/off-line contents are being converted into mobile contents. Although the cartoon contents especially are one of the most popular mobile contents, it is difficult to provide users with the existing on/off-line contents without any considerations due to the small size of the mobile screen. In existing methods to overcome the problem, the cartoon contents on mobile devices are manually produced by computer software such as Photoshop. In this paper, we automatically produce the cartoon contents fitting for the small screen, and introduce a clustering method useful for variety types of cartoon images as a prerequisite stage for preserving semantic meaning. Texture information which is useful for gray-scale image segmentation gives us a good clue for semantic analysis and self-organizing feature maps (SOFM) is used to cluster similar texture information. Besides we automatically segment the clustered SOFM outputs using agglomerative clustering. In our experimental results, combined approaches show good results of clustering in several cartoons. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540393924
Database :
Complementary Index
Journal :
Multimedia Content Representation, Classification & Security
Publication Type :
Book
Accession number :
33001607
Full Text :
https://doi.org/10.1007/11848035_54