Manga (Japanese comics) are popular worldwide. However, current e-manga archives offer very limited search support, i.e., keyword-based search by title or author. To make the manga search experience more intuitive, efficient, and enjoyable, we propose a manga-specific image retrieval system. The proposed system consists of efficient margin labeling, edge orientation histogram feature description with screen tone removal, and approximate nearest-neighbor search using product quantization. For querying, the system provides a sketch-based interface. Based on the interface, two interactive reranking schemes are presented: relevance feedback and query retouch. For evaluation, we built a novel dataset of manga images, Manga109, which consists of 109 comic books of 21,142 pages drawn by professional manga artists. To the best of our knowledge, Manga109 is currently the biggest dataset of manga images available for research. Experimental results showed that the proposed framework is efficient and scalable (70 ms from 21,142 pages using a single computer with 204 MB RAM).
From an image-processing perspective, manga has a distinctive visual nature compared with natural images. Several applications for manga images have been proposed: colorization @cite_45 @cite_43 @cite_48 , vectorization @cite_66 , layout recognition @cite_80 , layout generation @cite_57 @cite_34 , element composition @cite_18 , manga-like rendering @cite_59 , speech balloon detection @cite_28 , segmentation @cite_53 , face tailored features @cite_26 , screen tone separation @cite_25 , retargeting @cite_5 , and manga-like summarization for video @cite_14 @cite_42 @cite_60 . The research group at Universit 'e de La Rochelle constructed a comic database @cite_61 , which we mention in sec:db , and analyzed the structure of visual elements in a page to understand the comics content semantically @cite_36 .
@cite_36 @cite_42 @cite_43 @cite_5 @cite_66 @cite_18 @cite_60 @cite_48 @cite_80 @cite_26 @cite_28 @cite_57 @cite_34 @cite_25 @cite_61 @cite_14 @cite_53 @cite_45 @cite_59
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We introduce in this paper a new approach that conveniently converts conversational videos into comics with manga-style layout. With our approach, the manga-style layout of a comic page is achieved in a content-driven manner, and the main components, including panels and word balloons, that constitute a visually pleasing comic page are intelligently organized . Our approach extracts key frames on speakers by using a speaker detection technique such that word balloons can be placed near the corresponding speakers. We qualitatively measure the information contained in a comic page. With the initial layout automatically determined, the final comic page is obtained by maximizing such a measure and optimizing the parameters relating to the optimal display of comics. An efficient Markov chain Monte Carlo sampling algorithm is designed for the optimization. Our user study demonstrates that users much prefer our manga-style comics to purely Western style comics. Extensive experiments and comparisons against previous work also verify the effectiveness of our approach. Manga layout is a core component in manga production, characterized by its unique styles. However, stylistic manga layouts are difficult for novices to produce as it requires hands-on experience and domain knowledge. In this paper, we propose an approach to automatically generate a stylistic manga layout from a set of input artworks with user-specified semantics, thus allowing less-experienced users to create high-quality manga layouts with minimal efforts. We first introduce three parametric style models that encode the unique stylistic aspects of manga layouts, including layout structure, panel importance, and panel shape. Next, we propose a two-stage approach to generate a manga layout: 1) an initial layout is created that best fits the input artworks and layout structure model, according to a generative probabilistic framework; 2) the layout and artwork geometries are jointly refined using an efficient optimization procedure, resulting in a professional-looking manga layout. Through a user study, we demonstrate that our approach enables novice users to easily and quickly produce higher-quality layouts that exhibit realistic manga styles, when compared to a commercially-available manual layout tool. Manga (Japanese comics) are popular all over the world. However, most existing manga are monochrome. If such monochrome manga can be colorized, readers can enjoy the richer representations. In this paper, we propose a semiautomatic colorization method for manga. Given a previously colored reference manga image and target monochrome manga images, we propagate the colors of the reference manga to the target manga by representing images as graphs and matching graphs. The proposed method enables coloring of manga images without time-consuming manual colorization. We show results in which the colors of characters were correctly transferred to target characters, even those with complex structures. Automatically extracting frames panels from digital comic pages is crucial for techniques that facilitate comic reading on mobile devices with limited display areas. However, automatic panel extraction for manga, i.e., Japanese comics, can be especially challenging, largely because of its complex panel layout design mixed with various visual symbols throughout the page. In this paper, we propose a robust method for automatically extracting panels from digital manga pages. Our method first extracts the panel block by closing open panels and identifying a page background mask. It then performs a recursive binary splitting to partition the panel block into a set of sub-blocks, where an optimal splitting line at each recursive level is determined adaptively.