text: 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).
  @cite_21: We introduce an approach for sketch classification based on Fisher vectors that significantly outperforms existing techniques. For the TU-Berlin sketch benchmark [ 2012a], our recognition rate is close to human performance on the same task. Motivated by these results, we propose a different benchmark for the evaluation of sketch classification algorithms. Our key idea is that the relevant aspect when recognizing a sketch is not the intention of the person who made the drawing, but the information that was effectively expressed. We modify the original benchmark to capture this concept more precisely and, as such, to provide a more adequate tool for the evaluation of sketch classification techniques. Finally, we perform a classification-driven analysis which is able to recover semantic aspects of the individual sketches, such as the quality of the drawing and the importance of each part of the sketch for the recognition.
  @cite_2: Because of the popularity of touch-screen devices, it has become a highly desirable feature to retrieve images from a huge repository by matching with a hand-drawn sketch. Although searching images via keywords or an example image has been successfully launched in some commercial search engines of billions of images, it is still very challenging for both academia and industry to develop a sketch-based image retrieval system on a billion-level database. In this work, we systematically study this problem and try to build a system to support query-by-sketch for two billion images. The raw edge pixel and Chamfer matching are selected as the basic representation and matching in this system, owning to the superior performance compared with other methods in extensive experiments. To get a more compact feature and a faster matching, a vector-like Chamfer feature pair is introduced, based on which the complex matching is reformulated as the crossover dot-product of feature pairs. Based on this new formulation, a compact shape code is developed to represent each image sketch by projecting the Chamfer features to a linear subspace followed by a non-linear source coding. Finally, the multi-probe Kmedoids-LSH is leveraged to index database images, and the compact shape codes are further used for fast reranking. Extensive experiments show the effectiveness of the proposed features and algorithms in building such a sketch-based image search system.
summary: In the manga feature description task, we compared the proposed method with (1) BoF, (2) large-window FV as the state-of-the-art of BoF-based methods @cite_21 , and (3) compact oriented chamfer matching (Compact OCM) as the state-of-the-art of chamfer-based methods @cite_2 .