Shrink image by feature matrix decomposition

With the development of multimedia technology, image resizing has been raised as a question when the aspect ratio of an examined image should be displayed on a device with a different aspect ratio. Traditional nonuniform scaling for tackling this problem will lead to distortion. Therefore, content-aware consideration is mostly incorporated in the designing procedure. Such methods generally defines an energy function indicating the importance level of image content. The more important regions would be retained in the resizing procedure and distortion could be avoided consequently. The definition of the related energy function is thus the critical factor that directly influences the resizing results. In this work, we explore the definition of energy function from another aspect, matrix decomposition of Low-rank Representation. In our processing, a feature matrix that reflects the texture prior of object contour is firstly constructed. Then the matrix is decomposed into a low-rank one and sparse one. The energy function for resizing is then inferred from the sparse one. We illustrate the proposed method through seam carving framework and image shrinkage operation. Experiments on a dataset containing 1000 images demonstrate the effectiveness and robustness of the proposed method.