Abstract
As a classic technique in the field of computer vision, image composition has been widely used and recognized. It could composite new regions extracted from candidate images into target images, which could achieve realistic composited results. However, in the process of composition, it is difficult for traditional composition methods to select enough semantically valid candidate images. Moreover, imperfect composition techniques apparently reduce the effectiveness of composition.Aiming at these problems, we proposed a sparse coding based candidate image selection model that could offer more semantically valid candidate images. At the same time, optimized composition techniques were also used for improving the composition quality. Finally, compared with traditional models, adequate experimental results using a large number of images indicate that our model not only could retrieve more effective candidate images but also could achieve consistent composition results.
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