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

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

Self-Play Reinforcement Learning for Fast Image Retargeting

In this study, we address image retargeting, which is a task that adjusts input images to arbitrary sizes. In one of the best-performing methods called MULTIOP, multiple retargeting operators were combined and retargeted images at each stage were generated to find the optimal sequence of operators that minimized the distance between original and retargeted images. The limitation of this method is in its tremendous processing time, which severely prohibits its practical use. Therefore, the purpose of this study is to find the optimal combination of operators within a reasonable processing time; we propose a method of predicting the optimal operator for each step using a reinforcement learning agent. The technical contributions of this study are as follows. Firstly, we propose a reward based on self-play, which will be insensitive to the large variance in the content-dependent distance measured in MULTIOP. Secondly, we propose to dynamically change the loss weight for each action to prevent the algorithm from falling into a local optimum and from choosing only the most frequently used operator in its training. Our experiments showed that we achieved multi-operator image retargeting with less processing time by three orders of magnitude and the same quality as the original multi-operator-based method, which was the best-performing algorithm in retargeting tasks.Comment: Accepted to ACM Multimedia 202

Content Aware Video Retargeting using Seam Carving

Video retargeting method achieves high - quality resizing to arbitrary aspect ratios for complex videos containing diverse camera and dynamic motions . Video retargeting from a full - resolution video to a lower resolution display will inevitably cause information loss. While retargeting the video the important contents must also be preserved. Seam carving works well for images without straight lines or regular patterns like landscape images but may cause distortions if used for images with straight lines. Our approach combines Seam Carving method along with Hough transform to preserve the origi nality of the video

Image resizing with minimum distortion

Displays became cheap and were combined with many devices, like camera, mobile, and so on…, so there has been an increased interest on resizing methods to make the image suitable and fill any screen size. Common and known methods like cropping or resampling can cause undesirable effects such as: losses in information or distortion in perception. Recently, content-aware image resizing methods have been proposed to get rid of these problems and produce exceptional results. Seam-carving produced by Avidan and Shamir has gained attention as an effective solution. This paper discussed about this method and used it to resize (minimize and maximize) four colored images vertically and horizontally respectively, and maintained the main features of the images by deleting or repeating only the uninfluenced features. The energy map was calculated that described the basic and influential details of the image using energy function. But instead of gradient function (as in Avidan and Shamir) entropy function was used to compute the energy of the images. A vertical or a horizontal seam of pixels with minimum energy values was either deleted or inserted to resize the image. Good results were obtained especially when the image contains spaces within its details. The work was programmed using Matlab2018a

Distortion Sensitive Algorithm to Preserve Line Structure Properties in Image Resampling

In order to remove less important content from image seam carving algorithm is used. In seam carving distortion is very low as compared to other techniques like scaling and cropping. The major drawback of seam carving is when seam intersects with straight line present in the image it distorts line structure; the line may become curve after distortion. This structure distortion not only degrades visual quality of image but also gives artifacts or aliased line structure. This paper presents a content aware seam carving algorithm to resize the image. After applying algorithm discussed the structure of regular objects present in the image can be preserved. In the proposed algorithm first line detection algorithm is applied over the image in order to detect possible straight lines present in the image. After detecting straight lines algorithm tries to find out intersection point of optimal seam with the straight line. Algorithm increases energy of local neighbourhood pixels of intersection point up to a predefined radius, so that no further seam can intersect same pixel again