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

Video based dynamic scene analysis and multi-style abstraction.

Tao, Chenjun.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 89-97).Abstracts in English and Chinese.Abstract --- p.iAcknowledgements --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Window-oriented Retargeting --- p.1Chapter 1.2 --- Abstraction Rendering --- p.4Chapter 1.3 --- Thesis Outline --- p.6Chapter 2 --- Related Work --- p.7Chapter 2.1 --- Video Migration --- p.8Chapter 2.2 --- Video Synopsis --- p.9Chapter 2.3 --- Periodic Motion --- p.14Chapter 2.4 --- Video Tracking --- p.14Chapter 2.5 --- Video Stabilization --- p.15Chapter 2.6 --- Video Completion --- p.20Chapter 3 --- Active Window Oriented Video Retargeting --- p.21Chapter 3.1 --- System Model --- p.21Chapter 3.1.1 --- Foreground Extraction --- p.23Chapter 3.1.2 --- Optimizing Active Windows --- p.27Chapter 3.1.3 --- Initialization --- p.29Chapter 3.2 --- Experiments --- p.32Chapter 3.3 --- Summary --- p.37Chapter 4 --- Multi-Style Abstract Image Rendering --- p.39Chapter 4.1 --- Abstract Images --- p.39Chapter 4.2 --- Multi-Style Abstract Image Rendering --- p.42Chapter 4.2.1 --- Multi-style Processing --- p.45Chapter 4.2.2 --- Layer-based Rendering --- p.46Chapter 4.2.3 --- Abstraction --- p.47Chapter 4.3 --- Experimental Results --- p.49Chapter 4.4 --- Summary --- p.56Chapter 5 --- Interactive Abstract Videos --- p.58Chapter 5.1 --- Abstract Videos --- p.58Chapter 5.2 --- Multi-Style Abstract Video --- p.59Chapter 5.2.1 --- Abstract Images --- p.60Chapter 5.2.2 --- Video Morphing --- p.65Chapter 5.2.3 --- Interactive System --- p.69Chapter 5.3 --- Interactive Videos --- p.76Chapter 5.4 --- Summary --- p.77Chapter 6 --- Conclusions --- p.81Chapter A --- List of Publications --- p.83Chapter B --- Optical flow --- p.84Chapter C --- Belief Propagation --- p.86Bibliography --- p.8

Saliency-based image enhancement

Ph.DDOCTOR OF PHILOSOPH