Image Retargeting Research Articles

Image Recomposition Using Seam Carving and Insertion Considering the Rule of Thirds

In this paper, we present an algorithm for adjusting the position of a user-specified object considering image aesthetics. Specifically, the user-specified object is positioned according to the rule of thirds by inserting or deleting unimportant seam lines from the image. To find such seam lines, a novel weight map is designed using the spatial and color distances from the object. We also design and analyze two approaches to seam carving and insertion. Experimental results show that the proposed method can be used as an effective semi-automatic image recomposition scheme.

Detection of Seam Carved Image Based on Additional Seam Carving Behavior

Seam carving is a kind of content aware image retargeting algorithm and can be applied to resize and deliberately remove objects from digital images. Based on the observation that after applying an additional seam carving operation, the similarity, the energy relative error, and the difference of seam distance of original image are quite different from those of the seam-carved image, we propose and develop a new method for detecting seam carving or seam insertion of natural images without knowledge of the original image. First, we apply an additional seam carving operation to the testing image, then calculate similarity, energy relative error, and difference of seam distance between the testing image and its seam carved version. Last, we extract 11 dimensional features to detect seam carving operation to train a support vector machine classifier for recognizing whether an image is an original or it has been modified using seam-carving. Our experimental results demonstrate that our proposed forensic method achieves not only better detection rate but also lower dimensional features compared with other existing seam carved detection methods.

A Retargeting Approach for Mesopic Vision: Simulation and Compensation

Retargeting approaches aim at providing a unified framework for image rendering in which both the intended scene luminance and the actual luminance of the display are taken into account. At the core of any color retargeting method, a color vision model and its inverse are employed. Such a color appearance model should be invertible and cover the entire luminance range of the human visual system. There are not many available models that meet these two conditions. Moreover, most of these models are developed based on psychophysical experiments over color patches, and many have never been used for complex images due to their complexity. In this article, a color retargeting approach based on the mesopic model of Shin et al. ["A color appearance model applicable in mesopic vision," Opt. Rev. 11, 272–278 (2004)] is developed to work with complex images. The authors propose an inverse model for complex images to compensate for color appearance changes on dimmed displays viewed in a dark environment. Their experimental results using both quantitative and qualitative evaluations show a discriminative improvement in the perceived color quality for mesopic vision. The proposed method can be incorporated into image retargeting techniques and display rendering mechanisms. © 2016 Society for Imaging Science and Technology.

Image Resizing using Seam Carving

Image Resizing using Seam Carving

Image Retargeting by Texture-Aware Synthesis.

Real-world images usually contain vivid contents and rich textural details, which will complicate the manipulation on them. In this paper, we design a new framework based on exampled-based texture synthesis to enhance content-aware image retargeting. By detecting the textural regions in an image, the textural image content can be synthesized rather than simply distorted or cropped. This method enables the manipulation of textural & non-textural regions with different strategies since they have different natures. We propose to retarget the textural regions by example-based synthesis and non-textural regions by fast multi-operator. To achieve practical retargeting applications for general images, we develop an automatic and fast texture detection method that can detect multiple disjoint textural regions. We adjust the saliency of the image according to the features of the textural regions. To validate the proposed method, comparisons with state-of-the-art image retargeting techniques and a user study were conducted. Convincing visual results are shown to demonstrate the effectiveness of the proposed method.

PERBANDINGAN ALGORITMA SHORTEST PATH DALAM PEMROSESAN CITRA DIGITAL SEAM CARVING

Seam carving is a method of content aware image resizing. As solutions shortest path algorithms are used to find images seams. Seam is a horizontal or vertical path of an image that has minimum energy. There are two (2) shortest path algorithms that will be discussed in this paper. This paper contains the results of shortest path algorithms comparison between Dijkstra and Directed Acyclic Graph to see which one is better than another in case of efficiency. The precomputed and recomputed methods will be compared to find the more efficient method for executing the seam carving transformation. A web application has been built for this purpose. This web app is capable of transforming image size with seam carving method. The complexity of Dijkstra and Acyclic will be compared to find which one is better. The result is Dijkstra has been won, with the O(4V) with Acyclicis O(5V). The use of precomputed and recomputed is evaluated by the conditions. If the preparation is evaluated then recomputed is more efficient, but if the preparation is not evaluated then the precomputed method is the better one and has faster performance.

Objective Quality Prediction of Image Retargeting Algorithms.

Quality assessment of image retargeting results is useful when comparing different methods. However, performing the necessary user studies is a long, cumbersome process. In this paper, we propose a simple yet efficient objective quality assessment method based on five key factors: i) preservation of salient regions; ii) analysis of the influence of artifacts; iii) preservation of the global structure of the image; iv) compliance with well-established aesthetics rules; and v) preservation of symmetry. Experiments on the RetargetMe benchmark, as well as a comprehensive additional user study, demonstrate that our proposed objective quality assessment method outperforms other existing metrics, while correlating better with human judgements. This makes our metric a good predictor of subjective preference.

Measuring and Predicting Visual Importance of Similar Objects.

Similar objects are ubiquitous and abundant in both natural and artificial scenes. Determining the visual importance of several similar objects in a complex photograph is a challenge for image understanding algorithms. This study aims to define the importance of similar objects in an image and to develop a method that can select the most important instances for an input image from multiple similar objects. This task is challenging because multiple objects must be compared without adequate semantic information. This challenge is addressed by building an image database and designing an interactive system to measure object importance from human observers. This ground truth is used to define a range of features related to the visual importance of similar objects. Then, these features are used in learning-to-rank and random forest to rank similar objects in an image. Importance predictions were validated on 5,922 objects. The most important objects can be identified automatically. The factors related to composition (e.g., size, location, and overlap) are particularly informative, although clarity and color contrast are also important. We demonstrate the usefulness of similar object importance on various applications, including image retargeting, image compression, image re-attentionizing, image admixture, and manipulation of blindness images.

Image resizing based on strengthened edges

Image resizing techniques based on image content have been extensively studied. Most of these approaches evaluate the importance of each pixel to preserve more critical regions and discard less critical regions for achieving a target image size. This study proposes an image resizing algorithm based on strengthened edges. First, we created two feature maps: an improved gradient map and an enhanced saliency map. We then computed a strengthened edge map on the basis of these two feature maps. Finally, the target image was constructed using a seam carving method on the basis of the strengthened edge map. More accurate results can be obtained using the proposed approach rather than previous methods.

Analysis of Wavelet Compression and Seam Carving Using the Hausdorf Distance

In this paper image quality of two types of compression methods, wavelet based and seam carving based are investigated. A metric is introduced to compare the image quality under wavelet and seam carving schemes. Meyer, Coiflet 2 and Jpeg2000 wavelet based methods are used as the wavelet based methods. Hausdorf distance based metric (HDM) is proposed and used for the comparison of the two compression methods instead of model based matching techniques and correspondence-based matching techniques, because there is no pairing of points in the two sets being compared. In addition entropy based metric (EM) or peak signal to noise ration based metric (PSNRM) cannot be used to compare the two schemes as the seam carving tends to deform the objects. The wavelet compressed images with different compression percentages were analyzed with HDM and EM and it was observed that HDM follows the EM/PSNRM for wavelet based compression methods. Then HDM is used to compare the wavelet and seam carved images for different compression percentages. The initial results showed that HDM is better metric for comparing wavelet based and seam carved images.

An improved genetic algorithms-based seam carving method

In a previous work, we proposed a new method to retarget images, i.e. to resize an image on both vertical and horizontal orientation, based on Genetic Algorithms called Genetic Seam Carving. The previous work presented a new individual modeling to represent connected pixels paths (seams) that were handled by Genetic Algorithms for image retargeting. This modeling has an important drawback, a fixed base-pixel position, the pivot. This condition is not interesting when the pivot is in a region of interest, such as an object one wants to remain in the image. Thus, our novel proposal presented in this paper aims at solving this issue, which could decrease the retargeting performance so that flexible seams are achieved and evolved. To do so, we also present new genetic operators for out target problem. As expected, our proposal outperforms the Seam Carving and the previous proposal in terms of image quality.

Stereoscopic Visual Attention Guided Seam Carving for Stereoscopic Image Retargeting

Stereoscopic image retargeting plays an important role in adaptive 3D stereoscopic displays. It aims to fit displays with various resolutions while preserving visually salient content and geometric consistency. We propose a stereoscopic image retargeting method based on stereoscopic visual attention guided seam carving. Firstly, stereoscopic saliency map is generated by combining 2D saliency and depth saliency maps, and significant energy map is generated by considering binocular disparity binocular and binocular just-noticeable-difference (BJND). Then, seam selection is applied to the left image based on stereoscopic saliency and energy maps, and seam replacement is performed for the occluded regions to prevent the geometry inconsistency. Finally, according to the matched left and right seams, the retargeted stereoscopic image is generated. In the experiments, subjective and objective analysis on three stereoscopic image databases shows that the proposed approach produces better seam carving results than the related existing methods.

An Effective Video Synopsis Approach with Seam Carving.

With a growth of surveillance cameras, the amount of captured videos expands. Manually analyzing and retrieving surveillance video is labor intensive and expensive. It would be much more convenient to generate a video digest, with which we can view the video in a fast and motion-preserving way. In this paper, we propose a novel video synopsis approach to generate condensed video, which uses an object tracking method for extracting important objects. This method will generate video tubes and a seam carving method to condense the original video. Experimental results demonstrate that our proposed method can achieve a high condensation rate while preserving all the important objects of interest. Therefore, this approach can enable users to view the surveillance video with great efficiency.

Image Retargeting for Preserving Robust Local Feature: Application to Mobile Visual Search

With the sharp increasing of mobile devices, conducting search on mobile devices becomes pervasive, and one of the most popular applications is mobile visual search. To achieve low bit-rate visual search, most of the existing works focus on addressing local descriptor coding and BoW histogram compression . In this paper, we extend the concept of image retargeting and propose a new image resizing approach that is devoted to preserving the robust local features in the query image while resizing it. Based on the extended concept, we introduce a novel mobile-visual-search scheme that conducts the proposed approach to reduce the size of the query image for achieving low bit-rate visual search. Extensive experiments on Oxford 5 K and Flickr 100k datasets show that our approach obtains superior retrieval performance than state-of-the-art image resizing approaches at the similar query size; meanwhile, it is cost effective in terms of processing time.

Detection of Seam-Carving Image Based on Benford's Law for Forensic Applications

Seam-Carving is widely used for content-aware image resizing. To cope with the digital image forgery caused by Seam-Carving, a new detecting algorithm based on Benford's law is presented. The algorithm utilize the probabilities of the first digits of quantized DCT coefficients from individual AC modes to detect Seam-Carving images. The experimental result shows that the performance of proposed method is better than that of the method based on traditional Markov features and other existing methods.

An improved genetic algorithms-based seam carving method

An improved genetic algorithms-based seam carving method

A Retargeting Approach for Mesopic Vision: Simulation and Compensation

Retargeting approaches aim at providing a unified framework for image rendering in which both the intended scene luminance and the actual luminance of the display are taken into account. At the core of any color retargeting method, a color vision model and its inverse are employed. Such a color appearance model should be invertible and cover the entire luminance range of the human visual system. There are not many available models that meet these two conditions. Moreover, most of these models are developed based on psychophysical experiments over color patches, and many have never been used for complex images due to their complexity. In this article, a color retargeting approach based on the mesopic model of Shin et al. [“A color appearance model applicable in mesopic vision,” Opt. Rev. 11, 272‐278 (2004)] is developed to work with complex images. The authors propose an inverse model for complex images to compensate for color appearance changes on dimmed displays viewed in a dark environment. Their experimental results using both quantitative and qualitative evaluations show a discriminative improvement in the perceived color quality for mesopic vision. The proposed method can be incorporated into image retargeting techniques and display rendering mechanisms.

Image Resizing by Multiscale Seam Carving in Wavelet Trees

Image Resizing by Multiscale Seam Carving in Wavelet Trees

Tamper Detection of JPEG Image Due to Seam Modifications

Content-aware image retargeting has been investigated since the last decade as a paradigm of image modification for proper display on the different screen sizes. Modifications, such as seam carving or seam insertion, have been introduced to achieve aforesaid image retargeting. The changes in an image are not easily recognizable by human eyes. Inspired by the blocking artifact characteristics matrix (BACM), a method to detect tampers caused by seam modification on JPEG retargeted images without knowledge of the original image is proposed in this paper. In a BACM block matrix, we found that the original JPEG image demonstrates a regular symmetrical data, whereas the symmetrical data in a block reconstructed by seam modification is destroyed. Twenty-two features are proposed to train the data using a support vector machine classification method. The experimental results clearly demonstrate that the proposed method provides a very high recognition rate for those JPEG retargeted images. The source codes and the complete experimental data can be accessed at http://video.minelab.tw/DETS/.

Sparse Seam-Carving for Structure Preserving Image Retargeting

The seam carving technique is one of the most popular techniques for content-aware image retargeting. There have been many attempts to improve existing methods of seam carving to provide more aesth...