Sketch-Based Image Retrieval via Adaptive Weighting

Ren, Shuai; Cheng, Jie-Zhi; Sun, Chang; Zhang, Yuejie

doi:10.1145/2578726.2578785

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…To reduce the impact of noisy edges, a line segment-based descriptor was proposed [22]. Chamfer matching, which is a successful method for computing the similarity between two contours of objects, was incorporated in the sketch-based retrieval framework [4], [7], [23].…”

Section: A Manga Image Descriptionmentioning

confidence: 99%

Sketch-based manga retrieval using manga109 dataset

Matsui

Ito

Aramaki

et al. 2016

Multimed Tools Appl

813

317

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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.

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Section: A Manga Image Descriptionmentioning

confidence: 99%

Sketch-based manga retrieval using manga109 dataset

Matsui

Ito

Aramaki

et al. 2016

Multimed Tools Appl

813

317

View full text Add to dashboard Cite

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“…Request permissions from Permissions@acm.org. interesting application is searching image databases using free-hand sketch queries [19,16].…”

Section: Introductionmentioning

confidence: 99%

“…Sketch is an important artistic drawing style and may be the simplest form since it is only composed of lines. An interesting application is searching image databases using free-hand sketch queries [19,16].…”

Section: Introductionmentioning

confidence: 99%

End-to-End Photo-Sketch Generation via Fully Convolutional Representation Learning

Li-liang

Wang

et al. 2015

Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

125

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Sketch-based face recognition is an interesting task in vision and multimedia research, yet it is quite challenging due to the great difference between face photos and sketches. In this paper, we propose a novel approach for photo-sketch generation, aiming to automatically transform face photos into detail-preserving personal sketches. Unlike the traditional models synthesizing sketches based on a dictionary of exemplars, we develop a fully convolutional network to learn the end-to-end photo-sketch mapping. Our approach takes whole face photos as inputs and directly generates the corresponding sketch images with efficient inference and learning, in which the architecture is stacked by only convolutional kernels of very small sizes. To well capture the person identity during the photo-sketch transformation, we define our optimization objective in the form of joint generativediscriminative minimization. In particular, a discriminative regularization term is incorporated into the photo-sketch generation, enhancing the discriminability of the generated person sketches against other individuals. Extensive experiments on several standard benchmarks suggest that our approach outperforms other state-of-the-arts in both photosketch generation and face sketch verification.

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Sketch2Manga: Sketch-based manga retrieval

Matsui

Aizawa

2014

2014 IEEE International Conference on Image Processing (ICIP)

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We propose a sketch-based method for manga image retrieval, in which users draw sketches via a Web browser that enables the automatic retrieval of similar images from a database of manga titles. The characteristics of manga images are different from those of naturalistic images. Despite the widespread attention given to content-based image retrieval systems, the question of how to retrieve manga images effectively has been little studied. We propose a fine multi-scale edge orientation histogram (FMEOH) whereby a number of differently sized squares on a page can be indexed efficiently. Our experimental results show that FMEOH can achieve greater accuracy than a state-of-the-art sketch-based retrieval method [1].

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Sketch-Based Image Retrieval via Adaptive Weighting

Cited by 6 publications

References 8 publications

Sketch-based manga retrieval using manga109 dataset

Sketch-based manga retrieval using manga109 dataset

End-to-End Photo-Sketch Generation via Fully Convolutional Representation Learning

Sketch2Manga: Sketch-based manga retrieval

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