Automatic cartoon image re-authoring using SOFM

Authors:
Eunjung Han;Anjin Park;Keechul Jung
Affiliations:
HCI Lab., School of Media, College of Information Technology, Soongsil University, Seoul, South Korea;HCI Lab., School of Media, College of Information Technology, Soongsil University, Seoul, South Korea;HCI Lab., School of Media, College of Information Technology, Soongsil University, Seoul, South Korea
Venue:
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Year:
2006

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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.