Sketch-based manga retrieval using manga109 dataset
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TLDR
A manga-specific image retrieval system that consists of efficient margin labeling, edge orientation histogram feature description with screen tone removal, and approximate nearest-neighbor search using product quantization is proposed.Abstract:
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).read more
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References
More filters
Journal ArticleDOI
The Inverted Multi-Index
Artem Babenko,Victor Lempitsky +1 more
TL;DR: Inverted multi-indices were able to significantly improve the speed of approximate nearest neighbor search on the dataset of 1 billion SIFT vectors compared to the best previously published systems, while achieving better recall and incurring only few percent of memory overhead.
Proceedings ArticleDOI
Spatial-bag-of-features
TL;DR: The proposed retrieval framework works well in image retrieval task owing to the encoding of geometric information of objects for capturing objects' spatial transformation, the supervised feature selection and combination strategy for enhancing the discriminative power, and the representation of bag-of-features for effective image matching and indexing for large scale image retrieval.
Journal ArticleDOI
Towards Large-Scale Histopathological Image Analysis: Hashing-Based Image Retrieval
TL;DR: A scalable image-retrieval framework is built based on the supervised kernel hashing technique and validated on several thousand histopathological images acquired from breast microscopic tissues, achieving about 88.1% classification accuracy as well as promising time efficiency.
Journal ArticleDOI
Multimedia search reranking: A literature survey
TL;DR: Categorize and evaluate algorithms for visual search reranking, which reorders visual documents based on multimodal cues to improve initial text-only searches, and discuss relevant issues such as data collection, evaluation metrics, and benchmarking.
Proceedings ArticleDOI
Packing bag-of-features
TL;DR: An approximate representation of bag-of-features obtained by projecting the corresponding histogram onto a set of pre-defined sparse projection functions, producing several image descriptors is proposed, which is at least one order of magnitude faster than standard bag- of-features while providing excellent search quality.