3 research outputs found
Sketch-based Manga Retrieval using Manga109 Dataset
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 content-based
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 sketch-based 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.Comment: 13 page
Dense RepPoints: Representing Visual Objects with Dense Point Sets
We present a new object representation, called Dense RepPoints, that utilizes
a large set of points to describe an object at multiple levels, including both
box level and pixel level. Techniques are proposed to efficiently process these
dense points, maintaining near-constant complexity with increasing point
numbers. Dense RepPoints is shown to represent and learn object segments well,
with the use of a novel distance transform sampling method combined with
set-to-set supervision. The distance transform sampling combines the strengths
of contour and grid representations, leading to performance that surpasses
counterparts based on contours or grids. Code is available at
\url{https://github.com/justimyhxu/Dense-RepPoints}
Fan Shape Model for Object Detection
We propose a novel shape model for object detection called Fan Shape Model (FSM). We model contour sample points as rays of final length emanating for a reference point. As in folding fan, its slats, which we call rays, are very flexible. This flexibility allows FSM to tolerate large shape variance. However, the order and the adjacency relation of the slats stay invariant during fan deformation, s-ince the slats are connected with a thin fabric. In analogy, we enforce the order and adjacency relation of the rays to stay invariant during the deformation. Therefore, FSM preserves discriminative power while allowing for a substantial shape deformation. FSM allows also for precise scale estimation during object detection. Thus, there is not need to scale the shape model or image in order to perform object detection. Another advantage of FSM is the fact that it can be applied directly to edge images, since it does not require any linking of edge pixels to edge fragments (contours). 1