Title: Manifold multi-view learning for cartoon alignment

Authors: Wei Li; Huosheng Hu; Chao Tang; Yuping Song

Addresses: School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China ' School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, Essex, UK ' Department of Computer Science and Technology, Hefei University, Hefei 230601, Anhui, China ' School of Mathematical Science, Xiamen University, Xiamen 361005, Fujian, China

Abstract: Cartoon alignment is a key to retrieve cartoon characters and synthesise new cartoon clips. To successfully achieve the tasks, it is necessary to extract visual features that comprehensively denote cartoon characters and to align the feature points accurately between cartoon characters. In this paper, Speed Up Robust Feature (SURF) and Shape Context (SC) are introduced to characterise the cartoon character from multi-view. To increase accuracy rate of cartoon character alignment, semi-supervised alignment and Procrustes alignment require predetermining the correspondence. To overcome the flaw, we propose a Manifold Multi-View Learning (MML) to align cartoon characters. MML learns a projection that maps data instance (from cartoon characters with different dimensionality) to a lower-dimensional space, which simultaneously matches the local geometry and preserves the neighbourhood relationship within each cartoon character. The matching relationship can be obtained from local geometry structure. Experimental results show the good performance.

Keywords: cartoon alignment; manifold; multi-view; speed up robust feature; shape context.

DOI: 10.1504/IJCAT.2020.104690

International Journal of Computer Applications in Technology, 2020 Vol.62 No.2, pp.91 - 101

Received: 09 Aug 2018
Accepted: 12 May 2019
Published online: 28 Jan 2020 *

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