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Information Technology Journal

Year: 2013 | Volume: 12 | Issue: 12 | Page No.: 2342-2349
Research Article

2-D Cartoon Character Detection based on Scalable-Shape Context and Hough Voting

Tiejun Zhang
School of Computer Science and Technology, Harbin Institute of Technology, 150080 Harbin, China
Qi Han
School of Computer Science and Technology, Harbin Institute of Technology, 150080 Harbin, China
Ahmed A. Abd El-Latif
School of Computer Science and Technology, Harbin Institute of Technology, 150080 Harbin, China
Xuefeng Bai
Harbin Institute of Technology, Shenzhen Graduate School, 518055 Guangdong, China
Xiamu Niu
School of Computer Science and Technology, Harbin Institute of Technology, 150080 Harbin, China
Cartoon pirate uploading is a very serious problem for the image and video-sharing website. In this study, we propose a new method to detect the characters in 2D-cartoon images, aiming at rejecting pirate uploading automatically. We extract the curve in the cartoon image as the main content and then design a local shape feature named Scalable-Shape Context (SSC) to present the local shape of cartoon. Firstly, we use the Harris-Laplace corner detector to find the key points at multi-scale in the cartoon image, most of which are localized at the junctions of curves. Secondly, the scale of each key point is used as a reference scale for Shape Context (SC) to describe the curvilinear structure around the key points. Then, the matching problem between the key points extracted from the input model and testing image is solved as an optimal assignment problem. Finally, a Hough-voting scheme is employed to find the location of the character in the testing image. The experimental results show that the proposed SSC-based detection method is effective in the detection of 2D-cartoon characters.
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