Figure 1: A seam is a connected path of low energy pixels in an image. On the left is the original image with one horizontal and one vertical seam. In the middle the energy function used in this example is shown (the magnitude of the gradient), along with the vertical and horizontal path maps used to calculate the seams. By automatically carving out seams to reduce image size, and inserting seams to extend it, we achieve content-aware resizing. The example on the top right shows our result of extending in one dimension and reducing in the other, compared to standard scaling on the bottom right. Effective resizing of images should not only use geometric constraints, but consider the image content as well. We present a simple image operator called seam carving that supports content-aware image resizing for both reduction and expansion. A seam is an optimal 8-connected path of pixels on a single image from top to bottom, or left to right, where optimality is defined by an image energy function. By repeatedly carving out or inserting seams in one direction we can change the aspect ratio of an image. By applying these operators in both directions we can retarget the image to a new size. The selection and order of seams protect the content of the image, as defined by the energy function. Seam carving can also be used for image content enhancement and object removal. We support various visual saliency measures for defining the energy of an image, and can also include user input to guide the process. By storing the order of seams in an image we create multi-size images, that are able to continuously change in real time to fit a given size.

We propose a principled approach to summarization of visual data (images or video) based on optimization of a well-defined similarity measure. The problem we consider is re-targeting (or summarization) of image/video data into smaller sizes. A good “visual summary ” should satisfy two properties: (1) it should contain as much as possible visual information from the input data; (2) it should introduce as few as possible new visual artifacts that were not in the input data (i.e., preserve visual coherence). We propose a bi-directional similarity measure which quantitatively captures these two requirements: Two signals S and T are considered visually similar if all patches of S (at multiple scales) are contained in T, and vice versa. The problem of summarization/re-targeting is posed as an optimization problem of this bi-directional similarity measure. We show summarization results for image and video data. We further show that the same approach can be used to address a variety of other problems, including automatic cropping, completion and synthesis of visual data, image collage, object removal, photo reshuffling and more. 1.

We present a novel image resizing method which attempts to ensure that important local regions undergo a geometric similarity transformation, and at the same time, to preserve image edge structure. To accomplish this, we define handles to describe both local regions and image edges, and assign a weight for each handle based on an importance map for the source image. Inspired by conformal energy, which is widely used in geometry processing, we construct a novel quadratic distortion energy to measure the shape distortion for each handle. The resizing result is obtained by minimizing the weighted sum of the quadratic distortion energies of all handles. Compared to previous methods, our method allows distortion to be diffused better in all directions, and important image edges are well-preserved. The method is efficient, and offers a closed form solution.

Watching TV shows on cell phones is starting to become a reality. Nevertheless, there still exist some significant issues due to the small size of cell phone screens. The direct transfer of contents that are not specifically shot for the mobile device will provide indistinguishable objects. An automated way, delivering the best viewing experience is proposed in this paper. This solution significantly improves the visual comfort, by zooming in on the regions of interest. The relevance of this solution rests on its capability to preserve the visually important areas as well as the temporal stability. Eye-tracking experiments are one metric to assess the reframing quality. Involving 16 observers, they show that more than 90 % of the visually important regions are kept in the reframed clip. pictures and involve a visual attention model. In [3], they proposed to use the presentation technique, called RSVP (Rapid Serial Visual Presentation). This presentation scans sequentially the regions of interest as a human observer will do. Another approach [8] rests on the use of higher level detectors as face or skin detectors. Those detectors bring cognitive information in order to improve the results stemming from a purely bottom-up visual attention or to drive directly the reframing strategy. The rationale of this approach lies simply on the fact that humans ’ eye are effortlessly attracted by faces. 1.

Abstract—Image retargeting aims to adapt images to displays of small sizes and different aspect ratios. Effective retargeting requires emphasizing the important content while retaining surrounding context with minimal visual distortion. In this paper, we present such an effective image retargeting method using saliency-based mesh parametrization. Our method first constructs a mesh image representation that is consistent with the underlying image structures. Such a mesh representation enables easy preservation of image structures during retargeting since it captures underlying image structures. Based on this mesh representation, we formulate the problem of retargeting an image to a desired size as a constrained image mesh parametrization problem that aims at finding a homomorphous target mesh with desired size. Specifically, to emphasize salient objects and minimize visual distortion, we associate image saliency into the image mesh and regard image structure as constraints for mesh parametrization. Through a stretch-based mesh parametrization process we obtain the homomorphous target mesh, which is then used to render the target image by texture mapping. The effectiveness of our algorithm is demonstrated by experiments. Index Terms—Image retargeting, mesh parametrization, attention model. 1

Images play a very relevant role in our daily life. People now can easily shoot and share pictures thanks to the exponential growth of the portable medias, such as digital cameras, mobile phone... As the display size of those devices is relatively small, browsing large pictures remains difficult. Content repurposing is an elegant solution to deal with this problem. It consists in cropping the images in order to display only the most interesting parts of the picture. A new algorithm is proposed in this paper; the experiments described herein, leading to a qualitative and a quantitative assessment, show that the proposed solution outperforms the conventional method. 1.

This paper presents a simple and effective image retargeting method that preserves visually important parts while reducing unwanted distortions of an image. Our approach is based on a novel importance diffusion scheme, which propagates importance of removed pixels to their neighbors for preserving visual contexts and avoiding over-shrinkage of unimportant parts. Importance diffusion enables even a simple row/column removal method, which removes the least important rows/columns repeatedly, to produce visually pleasant results. It also provides control over the trade-off between uniform and non-uniform sampling for the row/column removal and seam carving methods. Experimental result demonstrates that importance diffusion successfully improves the retargeting results of row/column removal and seam carving. Index Terms — Image processing, image sampling, image retargeting

Content-aware resizing methods have recently been developed, among which, seam-carving has achieved the most widespread use. Seam-carving’s versatility enables deliberate object removal and benign image resizing, in which perceptually important content is preserved. Both types of modifications compromise the utility and validity of the modified images as evidence in legal and journalistic applications. It is therefore desirable that image forensic techniques detect the presence of seam-carving. In this paper we address detection of seam-carving for forensic purposes. As in other forensic applications, we pose the problem of seam-carving detection as the problem of classifying a test image in either of two classes: a) seam-carved or b) non-seam-carved. We adopt a pattern recognition approach in which a set of features is extracted from the test image and then a Support Vector Machine based classifier, trained over a set of images, is utilized to estimate which of the two classes the test image lies in. Based on our study of the seam-carving algorithm, we propose a set of intuitively motivated features for the detection of seam-carving. Our methodology for detection of seam-carving is then evaluated over a test database of images. We demonstrate that the proposed method provides the capability for detecting seam-carving with high accuracy. For images which have been reduced 30 % by benign seam-carving, our method provides a classification accuracy of 91%. Keywords: Content-aware resizing, seam-carving, image forensics, detection 1.

Due to the increased use of Unammed Aerial Vehicle (UAV) platforms in land-sea search and surveillance operations a suitable general technique for the automatic extraction of visually significant information is needed in order to augment current human-performed manual analysis of received video imagery. This paper presents a novel image processing based approach that builds on existing salient object detection work within related domains. Our proposed approach uses an image contrast map derived from the combination of seminal work in this area, multiscale mean-shift segmentation with additional histogram enhancement and additional multi-channel edge information. This is used to construct a robust saliency map from a given UAV aerial image in the presence of environmental, transmission and motion noise affecting image quality. The approach is generally targeted towards the detection of salient objects in the rural, uncluttered and relatively uniform environments. A range of results are presented over such representative environments. 25 th International UAV Systems ConferenceAutomatic Salient Object Detection In UAV Imagery 1.

We present a method to fully automatically fit videos in 16:9 format on 4:3 screens and vice versa. It can be applied to arbitrary aspect ratios and can be used to make videos suitable for mobile viewing devices with small and possibly uncommonly sized displays. The cropping sequence is optimised over time to create smooth transitions and thus leads to an excellent viewing experience. Current televisions have simple and often disturbing methods which either show the centre region of the image, distort the image, or pad it with black borders. The technique presented here can fully automatically find the “right ” viewing area for each image in a video sequence. It works in real-time with only very little time-shift. We employ different low-level features and a loglinear model to learn how to find the right area. The method is able to automatically decide whether padding with black borders is necessary or whether all relevant image areas fit on screen by cropping the image. Evaluation is done on ten videos from five different types of content and the baseline methods are clearly outperformed. 1.