Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96271
Title: On combining multiple features for cartoon character retrieval and clip synthesis
Authors: Yu, Jun
Liu, Dongquan
Tao, Dacheng
Seah, Hock Soon
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Yu, J., Liu, D., Tao, D., & Seah, H. S. (2012). On Combining Multiple Features for Cartoon Character Retrieval and Clip Synthesis. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(5), 1413-1427.
Series/Report no.: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
Abstract: How do we retrieve cartoon characters accurately? Or how to synthesize new cartoon clips smoothly and efficiently from the cartoon library? Both questions are important for animators and cartoon enthusiasts to design and create new cartoons by utilizing existing cartoon materials. The first key issue to answer those questions is to find a proper representation that describes the cartoon character effectively. In this paper, we consider multiple features from different views, i.e., color histogram, Hausdorff edge feature, and skeleton feature, to represent cartoon characters with different colors, shapes, and gestures. Each visual feature reflects a unique characteristic of a cartoon character, and they are complementary to each other for retrieval and synthesis. However, how to combine the three visual features is the second key issue of our application. By simply concatenating them into a long vector, it will end up with the so-called “curse of dimensionality,” let alone their heterogeneity embedded in different visual feature spaces. Here, we introduce a semisupervised multiview subspace learning (semi-MSL) algorithm, to encode different features in a unified space. Specifically, under the patch alignment framework, semi-MSL uses the discriminative information from labeled cartoon characters in the construction of local patches where the manifold structure revealed by unlabeled cartoon characters is utilized to capture the geometric distribution. The experimental evaluations based on both cartoon character retrieval and clip synthesis demonstrate the effectiveness of the proposed method for cartoon application. Moreover, additional results of content-based image retrieval on benchmark data suggest the generality of semi-MSL for other applications.
URI: https://hdl.handle.net/10356/96271
http://hdl.handle.net/10220/11417
ISSN: 1083-4419
DOI: http://dx.doi.org/10.1109/TSMCB.2012.2192108
Rights: © 2012 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

Google ScholarTM

Check

Altmetric

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.