text: Supervised deep learning techniques have achieved great success in various fields due to getting rid of the limitation of handcrafted representations. However, most previous image retargeting algorithms still employ fixed design principles such as using gradient map or handcrafted features to compute saliency map, which inevitably restricts its generality. Deep learning techniques may help to address this issue, but the challenging problem is that we need to build a large-scale image retargeting dataset for the training of deep retargeting models. However, building such a dataset requires huge human efforts. In this paper, we propose a novel deep cyclic image retargeting approach, called Cycle-IR, to firstly implement image retargeting with a single deep model, without relying on any explicit user annotations. Our idea is built on the reverse mapping from the retargeted images to the given input images. If the retargeted image has serious distortion or excessive loss of important visual information, the reverse mapping is unlikely to restore the input image well. We constrain this forward-reverse consistency by introducing a cyclic perception coherence loss. In addition, we propose a simple yet effective image retargeting network (IRNet) to implement the image retargeting process. Our IRNet contains a spatial and channel attention layer, which is able to discriminate visually important regions of input images effectively, especially in cluttered images. Given arbitrary sizes of input images and desired aspect ratios, our Cycle-IR can produce visually pleasing target images directly. Extensive experiments on the standard RetargetMe dataset show the superiority of our Cycle-IR. In addition, our Cycle-IR outperforms the Multiop method and obtains the best result in the user study. Code is available at this https URL.
  @cite_30: Video retargeting is a crowded but challenging research area. In order to maximally comfort the viewers' watching experience, the most challenging issue is how to retain the spatial shape of important objects while ensure temporal smoothness and coherence. Existing retargeting techniques deal with these spatial-temporal requirements individually, which preserve the spatial geometry and temporal coherence for each region. However, the spatial-temporal property of the video content should be context-relevant, i.e., the regions belonging to the same object are supposed to undergo uniform spatial-temporal transformation. Regardless of the contextual information, the divide-and-rule strategy of existing techniques usually incurs various spatial-temporal artifacts. In order to achieve satisfactory spatial-temporal coherent video retargeting, in this paper, a novel context-aware solution is proposed via graph model. First, we employ a grid-based warping framework to preserve the spatial structure and temporal motion trend at the unit of grid cell. Second, we propose a graph-based motion layer partition algorithm to estimate motions of different regions, which simultaneously provides the evaluation of contextual relationship between grid cells while estimating the motions of regions. Third, complementing the salience-based spatial-temporal information preservation, two novel context constraints are encoded for encouraging the grid cells of the same object to undergo uniform spatial and temporal transformation, respectively. Finally, we formulate the objective function as a quadratic programming problem. Our method achieves a satisfactory spatial-temporal coherence while maximally avoiding the influence of artifacts. In addition, the grid-cell-wise motion estimation could be calculated every few frames, which obviously improves the speed. Experimental results and comparisons with state-of-the-art methods demonstrate the effectiveness and efficiency of our approach.
  @cite_13: We here introduce an image video retargeting method that operates arbitrary aspect ratios resizing achieved in real-time performances. Most of the literature retargeting approaches sacrifice real-time performances in behalf of quality. On the other hand, existing fast methods provide arguable results. We can obtain a valuable trade-off between effectiveness and efficiency. The method named Spring Simulation Retargeting (SSR) is mainly based on a physical springs-based simulation. The media are assumed as flexible objects composed of particles and springs with different local stiffness properties, related to the visual importance of the content. The variation of the object size generates elastic forces which determine a new arrangement of the particles, according to the elongation of their connected springs. The deformations are mostly introduced in regions where low importance content is present, while high saliency regions are preserved as desired. The proposed method is evaluated and compared both for images and videos, against several state of the art methods and a user study is taken to assess the results, showing the value of the approach.
  @cite_26: 
  @cite_8: We propose a new energy function for seam carving based on forward gradient differences to preserve regular structures in images. The energy function measures the curvature inconsistency between the pixels that become adjacent after seam removal, and involves the difference of gradient orientation and magnitude of the pixels. Our objective is to minimize the differences induced by the removed seam, and the optimization is performed by dynamic programming based on multiple cumulative energy maps, each of which corresponds to the seam pattern associated with a pixel. The proposed technique preserves straight lines and regular shapes better than the original and improved seam carving, and can be easily combined with other types of energy functions within the seam carving framework. We evaluated the performance of our algorithm by comparing with the original and improved seam carving algorithms using public data.
  @cite_48: 
  @cite_53: 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.
  @cite_3: Image resizing can be more effectively achieved with a better understanding of image semantics. In this paper, similar patterns that exist in many real-world images. are analyzed. By interactively detecting similar objects in an image, the image content can be summarized rather than simply distorted or cropped. This method enables the manipulation of image pixels or patches as well as semantic objects in the scene during image resizing process. Given the special nature of similar objects in a general image, the integration of a novel object carving operator with the multi-operator framework is proposed for summarizing similar objects. The object removal sequence in the summarization strategy directly affects resizing quality. The method by which to evaluate the visual importance of the object as well as to optimally select the candidates for object carving is demonstrated. To achieve practical resizing applications for general images, a template matching-based method is developed. This method can detect similar objects even when they are of various colors, transformed in terms of perspective, or partially occluded. To validate the proposed method, comparisons with state-of-the-art resizing techniques and a user study were conducted. Convincing visual results are shown to demonstrate the effectiveness of the proposed method.
  @cite_24: In this paper, we propose a novel image retargeting algorithm to resize images based on the extracted saliency information from the compressed domain. Firstly, we utilize DCT coefficients in JPEG bitstream to perform saliency detection with the consideration of the human visual sensitivity. The obtained saliency information is used to determine the relative visual importance of each 8 x 8 block for the image. Furthermore, we propose a new adaptive block-level seam removal operation for connected blocks to resize the image. Thanks to the directly derived saliency information from the compressed domain, the proposed image retargeting algorithm effectively preserves the objects of attention, efficiently removes the less crucial regions, and therefore significantly outperforms the relevant state-of-the-art algorithms, as demonstrated with the careful analysis and in the extensive experiments.
  @cite_44: 
  @cite_50: 
  @cite_34: Image retargeting algorithms aim to adapt the image to the display screen with the goal of preserving the image content as much as possible. However, existing methods and research efforts have mostly been directed towards retargeting algorithms that retarget images to rectangular domains. This significantly hampers its application to broader classes of display devices and platforms for which the display area can be of any origins and shapes. For example, seam carving-based methods retarget images by carving out seams that run from the top to the bottom of the images, and this results in changing the width and therefore aspect ratio of the image without changing the shape of the image boundary in any essential way. However, by carving out appropriately-chosen seam segments, seams that are not required to cut across the entire image, it is then possible to retarget the images to a broader array of image domains with non-rectangular boundaries. Based on this simple idea of carving out the seam segments, the main contribution of this paper is a novel image retargeting algorithm that is capable of retargeting images to non-rectangular domains. We evaluate the effectiveness of the proposed method on a number of challenging indoor and outdoor scene images, and the results demonstrate that the proposed algorithm is both efficient and effective, and it is capable of providing good-quality retargeted images for a variety of interesting boundary shapes.
  @cite_51: Image retargeting algorithms often create visually disturbing distortion. We introduce the property of scene consistency, which is held by images which contain no object distortion and have the correct object depth ordering. We present two new image retargeting algorithms that preserve scene consistency. These algorithms make use of a user-provided relative depth map, which can be created easily using a simple GrabCut-style interface. Our algorithms generalize seam carving. We decompose the image retargeting procedure into (a) removing image content with minimal distortion and (b) re-arrangement of known objects within the scene to maximize their visibility. Our algorithms optimize objectives (a) and (b) jointly. However, they differ considerably in how they achieve this. We discuss this in detail and present examples illustrating the rationale of preserving scene consistency in retargeting.
  @cite_25: 
summary: In this section, we review previous works related to this paper. Over the past decade, numerous image retargeting works have been proposed @cite_53 @cite_24 @cite_51 @cite_34 @cite_8 @cite_3 @cite_48 @cite_26 @cite_44 @cite_25 @cite_30 @cite_13 . The majority of image retargeting methods are based on hand-crafted visual attention map such as gradient map, color contrast, object segmentation, face detection, . We refer the readers to survey literature @cite_50 for more details. Here, we mainly discuss representative image retargeting methods and a recent deep learning based method. Besides, we will review the successful applications of cycle constraint in other fields.