Fast and improved seam carving with strip partition and neighboring probability constraints
Seam carving is an effective way of image retargeting. However, existing seam carving schemes often come with unacceptable artifacts and are quite time consuming. In this paper, we propose a fast seam carving scheme with strip partition and neighboring probability constraints to resolve these two problems simultaneously. Firstly, we split the original image into several strips of equal space and we estimate the importance of each strip by its average saliency values. This partition results that more seams are removed from the strips consisting of more unimportant regions while fewer seams are removed from that of more important regions. Then, we establish the adjacency relationship by maximum correlation [8]. The neighboring probability is obtained to describe the neighboring relationship between the seams. Finally, by combining the neighboring probability and their accumulated energy, least important seams are removed. The neighboring probability constraint ensures that the seam removal is distributed to avoid abrupt changes in the scene. This leads to an improved quality in the resized image. The experimental results show that the proposed approach performs better than the state-of-the-art seam carving schemes.