We propose an improved content-aware image resizing method that uses deep learning. The proposed method is extended from seam carving, which is another image resizing method. Seam carving uses the energy map from an image. It also removes a seam where the energy is the minimum. We propose a method for creating a deep energy map using an encoder-decoder convolution neural network. A deep energy map preserves important parts or boundaries in an image, without distortion. Furthermore, it has the characteristic that uniform intensity of edges is displayed for all images. Four well-known resizing methods and our proposed method were evaluated in terms of aspect ratio similarity. In such an objective evaluation, the proposed method demonstrated better results than the other four algorithms. Our proposed method can reduce the size of an image without damaging the overall structure or losing important information in the image.
Bibliographical noteFunding Information:
This work was supported by the Institute for Information and Communications Technology Promotion (IITP) Grant funded by the Korean Government (MSIP) (Development of the High-Precision Natural 3D View Generation Technology Using Smart-Car Multi-Sensors and Deep Learning) under Grant 2016-0-00197.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Materials Science(all)