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Y.W.Guo et al./Improving Photo Composition Elegantly:Considering Image Similarity During Composition Optimization (a)The input (b)Result in [BSS10] (c)Our result Figure 6:Comparison with the method in [BSS10J.(a)The input image in [BSS101.(b)The result produced by the method of (BSS101.Note that,the building is originally located on the grassland in the distance.However,its location is changed to the yellow land near the viewpoint in the result of [BSS10J.This in fact changes image semantics.(c)Our result. mize the composition of photos of different sizes,if only a 6.Conclusions pair of seams is processed each time.However if we carve out and insert several seams each time,it takes around 1 sec. We have presented a new algorithm for improving image The algorithm can be further accelerated by transplanting it compositions by optimizing a unified objective model of onto GPUs and exploiting the parallel computing power of composition aesthetics and image similarity.An edge-based modern Graphics card. measure of structural similarity that compares the optimized image and the original one is used.With the similarity con- Limitation.We use seam carving as the basic operation straint,our algorithm ensures visual similarity,and to some for improving image compositions.Seam carving will break extent,semantic consistency between the optimized images dense structures when the input image has complex struc- and the results.By searching the maximum of the objective tures and the seams will pass through them inevitably.It model,we are able to generate the composition improved is one drawback of our algorithm.We show a failure re- images with nearly unperceivable visual distortions.Our al- sult for an input image shot in the Grand Canyon,as shown gorithm is simple,intuitive,and easy to implement. in Figure 8.Since the fence runs through the image from left to right,the barbed wire is broken if too many ver- Since our algorithm mainly concentrates on how to en- tical seams passing through it are removed,even though sure image similarity in the process of improving compo- edge similarity is considered in our edge-based SSIM mea- sition,we now compute composition aesthetics only under sure.To handle this problem,it will be helpful to ex- the guidance of rule of thirds.Professional photographers, ploit other structure-preservation image retargeting meth- however,may have various disciplines and usually take pho- ods [GLS*09.ZCHM09]. tos according to their rich experiences.In order to achieve this,previous techniques have used a learning model to map the aesthetic features to user input image attractiveness or adopted more photography guidelines for compensating rule of thirds.We intend to integrate their methods of evaluat- ing composition aesthetics into our framework in future.In addition,it would be interesting to combine composition op- timization with those tone adjustment and color harmoniza- tion techniques for making photographs by non-professional photographers professional and attractive. 7.Acknowledgments (a) (b) The authors would like to thank the anonymous review- ers for their valuable and constructive comments.This Figure 8:A failure case.The barbed wire is broken in the result work was supported in part by the National Science Foun- (b).See the blue rectangle. dation of China under Grants 61073098 and 61021062 and the National Fundamental Research Program of China (2010CB327903). ©2012 The Author()s 2012 The Eurographics Association and Blackwell Publishing Lid.Y. W. Guo et al. / Improving Photo Composition Elegantly: Considering Image Similarity During Composition Optimization (a) The input (b) Result in [BSS10] (c) Our result Figure 6: Comparison with the method in [BSS10]. (a) The input image in [BSS10]. (b) The result produced by the method of [BSS10]. Note that, the building is originally located on the grassland in the distance. However, its location is changed to the yellow land near the viewpoint in the result of [BSS10]. This in fact changes image semantics. (c) Our result. mize the composition of photos of different sizes, if only a pair of seams is processed each time. However if we carve out and insert several seams each time, it takes around 1 sec. The algorithm can be further accelerated by transplanting it onto GPUs and exploiting the parallel computing power of modern Graphics card. Limitation. We use seam carving as the basic operation for improving image compositions. Seam carving will break dense structures when the input image has complex structures and the seams will pass through them inevitably. It is one drawback of our algorithm. We show a failure result for an input image shot in the Grand Canyon, as shown in Figure 8. Since the fence runs through the image from left to right, the barbed wire is broken if too many vertical seams passing through it are removed, even though edge similarity is considered in our edge-based SSIM measure. To handle this problem, it will be helpful to exploit other structure-preservation image retargeting methods [GLS∗09,ZCHM09]. (a) (b) Figure 8: A failure case. The barbed wire is broken in the result (b). See the blue rectangle. 6. Conclusions We have presented a new algorithm for improving image compositions by optimizing a unified objective model of composition aesthetics and image similarity. An edge-based measure of structural similarity that compares the optimized image and the original one is used. With the similarity constraint, our algorithm ensures visual similarity, and to some extent, semantic consistency between the optimized images and the results. By searching the maximum of the objective model, we are able to generate the composition improved images with nearly unperceivable visual distortions. Our algorithm is simple, intuitive, and easy to implement. Since our algorithm mainly concentrates on how to ensure image similarity in the process of improving composition, we now compute composition aesthetics only under the guidance of rule of thirds. Professional photographers, however, may have various disciplines and usually take photos according to their rich experiences. In order to achieve this, previous techniques have used a learning model to map the aesthetic features to user input image attractiveness or adopted more photography guidelines for compensating rule of thirds. We intend to integrate their methods of evaluating composition aesthetics into our framework in future. In addition, it would be interesting to combine composition optimization with those tone adjustment and color harmonization techniques for making photographs by non-professional photographers professional and attractive. 7. Acknowledgments The authors would like to thank the anonymous reviewers for their valuable and constructive comments. This work was supported in part by the National Science Foundation of China under Grants 61073098 and 61021062, and the National Fundamental Research Program of China (2010CB327903). c 2012 The Author(s) c 2012 The Eurographics Association and Blackwell Publishing Ltd