Abstract
Panels are the fundamental elements of manga pages, and hence their detection serves as the basis of high-level manga content understanding. Existing panel detection methods could be categorized into heuristic-based methods and CNN-based (Convolutional Neural Network-based) ones. Although the former can accurately localize panels, they cannot handle well elaborate panels and require considerable effort to hand-craft rules for every new hard case. In contrast, detection results of CNN-based methods could be rough and inaccurate. We utilize CNN object detectors to propose coarse guide panels, then use heuristics to propose panel candidates and finally optimize an energy function to select the most plausible candidates. CNN assures roughly localized detection of almost all kinds of panels, while the follow-up procedure refines the detection results and minimizes the margin between detected panels and ground-truth with the help of heuristics and energy minimization. Experimental results show the proposed method surpasses previous methods regarding panel detection F1-score and page accuracy.
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Acknowledgments
This work is supported by National Natural Science Foundation of China under Grant 61673029. This work is also a research achievement of Key Laboratory of Science, Technology and Standard in Press Industry (Key Laboratory of Intelligent Press Media Technology). The authors gratefully acknowledge financial support from China Scholarship Council.
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Zhou, Y., Wang, Y., He, Z., Tang, Z., Suen, C.Y. (2020). Towards Accurate Panel Detection in Manga: A Combined Effort of CNN and Heuristics. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham. https://doi.org/10.1007/978-3-030-37731-1_18
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DOI: https://doi.org/10.1007/978-3-030-37731-1_18
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