Salient context aware for improved image resizing

Peijian Zhu; Yu Zhang; Jinglong Li; Jianping Peng; Liming Xie

doi:10.1117/12.3048590

5 December 2024 Salient context aware for improved image resizing

Peijian Zhu, Yu Zhang, Jinglong Li, Jianping Peng, Liming Xie

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Proceedings Volume 13418, Fifteenth International Conference on Information Optics and Photonics (CIOP 2024); 134182U (2024) https://doi.org/10.1117/12.3048590
Event: 15th International Conference on Information Optics and Photonics (CIOP2024), 2024, Xi’an, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Peijian Zhu, Yu Zhang, Jinglong Li, Jianping Peng, and Liming Xie "Salient context aware for improved image resizing", Proc. SPIE 13418, Fifteenth International Conference on Information Optics and Photonics (CIOP 2024), 134182U (5 December 2024); https://doi.org/10.1117/12.3048590

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KEYWORDS

Semantics

Feature extraction

Image processing

Distortion

Image filtering

Convolution

Volume rendering

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