Matching-Area-Based Seam Carving for Video Retargeting

Yan, Bo; Sun, Kairan; Liu, Liu

doi:10.1109/tcsvt.2012.2203740

Cited by 46 publications

(64 citation statements)

References 29 publications

(49 reference statements)

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“…The videos are a sequence of continuous frames, it is intuitive to employ the picture algorithm or image data to process the videos. Bo et al [12] employs the picture algorithm [1], extended to video volumes. Yang et al [15,17] leverage the images tags to tagging or index videos.…”

Section: Methodsmentioning

confidence: 99%

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Towards perceptual video cropping with curve fitting

Liu

Lin

et al. 2014

Multimed Tools Appl

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Coming with the proliferation of mobile devices, the issue of displaying videos with varying resolutions and aspect ratios has become emerging. To this end, state-of-the-art works typically resort to video retargeting or cropping, i.e. adaptively adjusting the display regions and contents within each video frame to fit for the mobile device screens. However, most existing approaches retain on evaluating the objective visual statistics to select salient parts to be cropped within each frame, with time-consuming constraints to ensure the temporal consistency, which is indeed unsuitable to cope with the massive videos uploaded daily from user-contributed platforms. In this paper, we propose an efficient yet robust video cropping algorithm which merits in two-fold: First, a perceptual Region of Interest selection approach is proposed by employing a novel Interesting Region Score, which is a linear combination of Rate of Focused Attention, Total Saliency Score, and Bias from Center Penalty. Second, we adopt fast curve fitting to seek for an optimal cropping sequence over a set of consecutive video frames within a shot, which avoids the complex optimization with temporal constraints among adjacent frames. Experiments on video sequences from Backkom cartoon and Iron Man II with comparisons against several alternative approaches demonstrate the efficiency and robustness of the proposed algorithm.

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Section: Methodsmentioning

confidence: 99%

“…As for retargeting method, Wolf et al [10] proposed an optimized transformation algorithm. Wu et al [11] and Yan et al [12] proposed seam carving [1] based approaches. These methods handle the problem at the level of pixels, and the correlation between pixels in the same and temporally adjacent frames are involved into the computation.…”

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confidence: 99%

Towards perceptual video cropping with curve fitting

Liu

Lin

et al. 2014

Multimed Tools Appl

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“…In our psycho-visual tests, participants were presented with an original image and two retargeting images side by side, and they were asked to answer which retargeting result they preferred. The users were kept naive about the purpose of the experiment and were not provided with any special technical instructions [11].…”

Section: User Studymentioning

confidence: 99%

Efficient Image Retargeting via Adaptive Pixel Fusion

Yan

Yang

2014

Proceedings of the 22nd ACM International Conference on Multimedia

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This paper presents a new image retargeting method, which is able to provide both high efficiency and quality. Firstly, our method calculates the image resizing factors in both horizontal and vertical directions. Then, it resizes the image in these both directions sequentially in order to achieve the target aspect ratio with the help of our proposed mapping functions. Finally, the reconstructed image can be uniformly scaled to the target resolution. Experimental results show that our method is not only cost effective in terms of computational resource, but also provides visually pleasing resized images.

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“…Although several retargeting methods have been developed, such as mesh-deformation-based techniques [4][5][6] and some combinations of multiple methods [7], seam carving still remains an effective tool due to the simplicity and the scalability. Seam carving has expanded in applications, such as for video sequences [8], stereo images [9], image compression [10,11], tamper detection [12,13], and particular images having high image detail concentration [14].…”

Section: Introductionmentioning

confidence: 99%

Gradient-based global features for seam carving

Ito

2016

J Image Video Proc.

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We propose a gradient-based global feature and its application to seam carving. We focus on areas, rather than points and lines, to be assigned as important elements for expressing the rough location of salient objects in an image. The proposed feature is calculated with a low computational load based on gray-scale intensity. The superior performance of the proposed gradient-based global feature, as compared to state-of-the-art salient features for seam carving, is demonstrated experimentally.

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Matching-Area-Based Seam Carving for Video Retargeting

Cited by 46 publications

References 29 publications

Towards perceptual video cropping with curve fitting

Towards perceptual video cropping with curve fitting

Efficient Image Retargeting via Adaptive Pixel Fusion

Gradient-based global features for seam carving

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