Y.W.Guo et al./Improving Photo Composition Elegantly:Considering Image Similarity During Composition Optimization 2942rs891020 0 忍m8ot黑an21o0 (a) (b) (c) (d) Figure 4:The resulting images (c)corresponding to the maximum of E shown in the red curves.(a)The inputs.(b)Variances of Ee (blue) Es (green),and E (red)with the number of seams inserted (removed).(d)The resulting images by maximizing Fe only.For some images with complex background structures,visual distortions will be introduced if the input image is optimized with respect to composition aesthetics only. An example is shown in the second row,and the resulting image of (d)exhibits obvious distortions in the background area. (a)The inputs (b)Results in [LJW10] (c)Our results Figure 5:Comparison with the method by Liu et al.[LW10].(a)The input images published in [(b)Their method might suffer from noticeable distortions in the results.The distortions in water regions in both examples and the shadow in the bottom example are remarkable (c)Our results. those images with complex background structures.Further- pends on image size and the location of interest object,and more,such scheme generally cannot handle the case where the major computation is spent on computing seams using foreground object is occluded by the background region.An dynamic programming and measuring SSIM between the in- example is the image in the Ist row of Figure 4 where mo- put image and the image series resulting from carving out torcycle tires are occluded by the fence.Our algorithm does and inserting seams.We use a fast multi-thread CPU imple- not need to segment the foreground object and works well mentation of seam carving,and also implement the fully par- for such images. allelized SSIM algorithm,on a 2.8GHz Dual Core PC with 4GB memory.Our algorithm takes 2 to 8 seconds to opti- Computational compelexity of our algorithm mainly de-Y. W. Guo et al. / Improving Photo Composition Elegantly: Considering Image Similarity During Composition Optimization 0 20 40 60 80 100 120 0.4 0.6 0.8 1 Number of Seams Ee Es E 0 20 40 60 80 100 0.4 0.6 0.8 1 Number of Seams Ee Es E (a) (b) (c) (d) Figure 4: The resulting images (c) corresponding to the maximum of E shown in the red curves. (a) The inputs. (b) Variances of Ee (blue), Es (green), and E (red) with the number of seams inserted (removed). (d) The resulting images by maximizing Ee only. For some images with complex background structures, visual distortions will be introduced if the input image is optimized with respect to composition aesthetics only. An example is shown in the second row, and the resulting image of (d) exhibits obvious distortions in the background area. (a) The inputs (b) Results in [LJW10] (c) Our results Figure 5: Comparison with the method by Liu et al. [LJW10]. (a) The input images published in [LJW10]. (b) Their method might suffer from noticeable distortions in the results. The distortions in water regions in both examples and the shadow in the bottom example are remarkable. (c) Our results. those images with complex background structures. Further￾more, such scheme generally cannot handle the case where foreground object is occluded by the background region. An example is the image in the 1st row of Figure 4 where mo￾torcycle tires are occluded by the fence. Our algorithm does not need to segment the foreground object and works well for such images. Computational compelexity of our algorithm mainly de￾pends on image size and the location of interest object, and the major computation is spent on computing seams using dynamic programming and measuring SSIM between the in￾put image and the image series resulting from carving out and inserting seams. We use a fast multi-thread CPU imple￾mentation of seam carving, and also implement the fully par￾allelized SSIM algorithm, on a 2.8GHz Dual Core PC with 4GB memory. Our algorithm takes 2 to 8 seconds to opti-
c 2012 The Author(s)
c 2012 The Eurographics Association and Blackwell Publishing Ltd