Sketch2Manga: Sketch-based manga retrieval

Matsui, Yusuke; Aizawa, Kiyoharu; Yu, Jing

doi:10.1109/icip.2014.7025626

Cited by 15 publications

(10 citation statements)

References 22 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar techniques based on the use of multi-scale methods are proposed in [23,24]. Different solutions based on the propagation of desired colors to similar pixels were proposed in [25][26][27][28][29][30][31][32][33][34]. Furthermore, a large set of composite pictures was analyzed to assess their naturalness [35].…”

Section: Recoloring Techniques For Images and Videosmentioning

confidence: 99%

Color Harmonization, Deharmonization and Balancing in Augmented Reality

2021

View full text Add to dashboard Cite

Color schemes play a crucial role in blending virtual objects with the real environment. Good color schemes improve user’s perception, which is of crucial importance for augmented reality. In this paper, we propose a set of novel methods based on the color harmonization methodology to recolor augmented reality content according to the real background. Three different strategies are proposed—harmonic, disharmonic, and balance—that allow for satisfying different needs in different settings depending on the application field. The first approach aims to harmonize the colors of virtual objects to make them consistent with the colors of the real background and reach a more pleasing effect to a human eye. The second approach, instead, can be adopted to generate a set of disharmonious colors with respect to real ones to be associated with the augmented virtual content to improve its distinctiveness from the real background. The third approach balances these goals by achieving a compromise between harmony and good visibility among virtual and real objects. Furthermore, the proposed re-coloring method is applied to three different case studies by adopting the three strategies to meet three different objectives, which are specific for each case study. Several parameters are calculated for each test, such as the covered area, the color distribution, and the set of generated colors. Results confirm the great potential of the proposed approaches to improve the AR visualization in different scenarios.

show abstract

Section: Recoloring Techniques For Images and Videosmentioning

confidence: 99%

Color Harmonization, Deharmonization and Balancing in Augmented Reality

2021

View full text Add to dashboard Cite

show abstract

“…Related Work Hashing techniques [16,33,58,38,34,17,70,35,14,66,36,44,37,51,25,32] have recently been successfully applied to encode high-dimensional features into compact similarity-preserving binary codes, which enables extremely fast similarity search by the use of Hamming distances. Inspired by this, some recent SBIR works [1,15,40,52,54,56] have incorporated existing hashing methods for efficient retrieval. For instance, LSH [16] and ITQ [17] are adopted to sketch-based image [1] and 3D model [15] retrieval tasks, respectively.…”

Section: Introductionmentioning

confidence: 99%

Deep Sketch Hashing: Fast Free-Hand Sketch-Based Image Retrieval

Liu

Shen

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

232

217

View full text Add to dashboard Cite

Free-hand sketch-based image retrieval (SBIR) is a specific cross-view retrieval task, in which queries are abstract and ambiguous sketches while the retrieval database is formed with natural images. Work in this area mainly focuses on extracting representative and shared features for sketches and natural images. However, these can neither cope well with the geometric distortion between sketches and images nor be feasible for large-scale SBIR due to the heavy continuous-valued distance computation. In this paper, we speed up SBIR by introducing a novel binary coding method, named Deep Sketch Hashing (DSH), where a semi-heterogeneous deep architecture is proposed and incorporated into an end-to-end binary coding framework. Specifically, three convolutional neural networks are utilized to encode free-hand sketches, natural images and, especially, the auxiliary sketch-tokens which are adopted as bridges to mitigate the sketch-image geometric distortion. The learned DSH codes can effectively capture the crossview similarities as well as the intrinsic semantic correlations between different categories. To the best of our knowledge, DSH is the first hashing work specifically designed for category-level SBIR with an end-to-end deep architecture. The proposed DSH is comprehensively evaluated on two large-scale datasets of TU-Berlin Extension and Sketchy, and the experiments consistently show DSH's superior SBIR accuracies over several state-of-the-art methods, while achieving significantly reduced retrieval time and memory footprint.

show abstract

“…5. BING [65], selective search [64], and sliding window (baseline) [8] are compared. From the results, we concluded that selective search is the best approach for this dataset.…”

Section: A Eoh Feature Description With Object Windowsmentioning

confidence: 99%

Sketch-based manga retrieval using manga109 dataset

Matsui

Ito

Aramaki

et al. 2016

Multimed Tools Appl

Self Cite

826

337

View full text Add to dashboard Cite

Manga (Japanese comics) are popular worldwide. However, current e-manga archives offer very limited search support, including keyword-based search by title or author, or tag-based categorization. To make the manga search experience more intuitive, efficient, and enjoyable, we propose a contentbased manga retrieval system. First, we propose a manga-specific image-describing framework. It consists of efficient margin labeling, edge orientation histogram feature description, and approximate nearest-neighbor search using product quantization. Second, we propose a sketch-based interface as a natural way to interact with manga content. The interface provides sketchbased querying, 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. We conducted a comparative study, a localization evaluation, and a large-scale qualitative study. From the experiments, we verified that: (1) the retrieval accuracy of the proposed method is higher than those of previous methods;(2) the proposed method can localize an object instance with reasonable runtime and accuracy; and (3) sketch querying is useful for manga search.

show abstract

Sketch2Manga: Sketch-based manga retrieval

Cited by 15 publications

References 22 publications

Color Harmonization, Deharmonization and Balancing in Augmented Reality

Color Harmonization, Deharmonization and Balancing in Augmented Reality

Deep Sketch Hashing: Fast Free-Hand Sketch-Based Image Retrieval

Sketch-based manga retrieval using manga109 dataset

Contact Info

Product

Resources

About