Using Regularity Unit As Guidance For Summarization-Based Image Resizing

In this paper, we propose a novel algorithm for summarization-based image resizing. In the past, a process of detecting precise locations of repeating patterns is required before the pattern removal step in resizing. However, it is difficult to find repeating patterns which are illuminated under different lighting conditions and viewed from different perspectives. To solve the problem, we first identify the regularity unit of repeating patterns by statistics. Then we can use the regularity unit for shift-map optimization to obtain a better resized image. The experimental results show that our method is competitive with other well-known methods.