Salient context aware for improved image resizing
Peijian Zhu, Yu Zhang, Jinglong Li, Jianping Peng, Liming Xie · 2024
An improved image wrapping method that preserves visually prominent features while resizing image into arbitrary scaling ratios is proposed. In this method, a salient context aware model is adopted to construct salient map to sign prominent image features at the aspect of texture and semantics. The salient map contains not only prominent texture with strong gradient changes but salient region with significant semantics. This model is implemented with an end-to-end convolutional neural network which maps input image to corresponding feature salient features. The proposed method simultaneously learns detailed and salient context and fuse both features as the salience mask. Then the seam carving method is utilized to implement image resizing with arbitrary ratios combined with the salience mask. Experimental results of the proposed and classic seam carving method indicate that our formulation has more robust and effective performance.