1,534 research outputs found
Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting
This paper proposes a weakly- and self-supervised deep convolutional neural
network (WSSDCNN) for content-aware image retargeting. Our network takes a
source image and a target aspect ratio, and then directly outputs a retargeted
image. Retargeting is performed through a shift map, which is a pixel-wise
mapping from the source to the target grid. Our method implicitly learns an
attention map, which leads to a content-aware shift map for image retargeting.
As a result, discriminative parts in an image are preserved, while background
regions are adjusted seamlessly. In the training phase, pairs of an image and
its image-level annotation are used to compute content and structure losses. We
demonstrate the effectiveness of our proposed method for a retargeting
application with insightful analyses.Comment: 10 pages, 11 figures. To appear in ICCV 2017, Spotlight Presentatio
Runtime-Flexible Multi-dimensional Arrays and Views for C++98 and C++0x
Multi-dimensional arrays are among the most fundamental and most useful data
structures of all. In C++, excellent template libraries exist for arrays whose
dimension is fixed at runtime. Arrays whose dimension can change at runtime
have been implemented in C. However, a generic object-oriented C++
implementation of runtime-flexible arrays has so far been missing. In this
article, we discuss our new implementation called Marray, a package of class
templates that fills this gap. Marray is based on views as an underlying
concept. This concept brings some of the flexibility known from script
languages such as R and MATLAB to C++. Marray is free both for commercial and
non-commercial use and is publicly available from www.andres.sc/marrayComment: Free source code availabl
Optimized Image Resizing Using Seam Carving and Scaling
International audienceWe present a novel method for content-aware image resizing based on optimization of a well-defined image distance function, which preserves both the important regions and the global visual effect (the background or other decorative objects) of an image. The method operates by joint use of seam carving and image scaling. The principle behind our method is the use of a bidirectional similarity function of image Euclidean distance (IMED), while cooperating with a dominant color descriptor (DCD) similarity and seam energy variation. The function is suitable for the quantitative evaluation of the resizing result and the determination of the best seam carving number. ifferent from the previous simplex-modeapproaches, our method takes the advantages of both discrete and continuous methods. The technique is useful in image resizing for both reduction/retargeting and enlarging. We also show that this approach can be extended to indirect image resizing
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