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Research Article | Open Access

Image resizing by reconstruction from deep features

Tel Aviv University, Tel Aviv, 69978, Israel
The Interdisciplinary Center Herzliya, Herzliya, 4610101, Israel

*Dov Danon and Moab Arar contribucted equally to this work.

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Abstract

Traditional image resizing methods usually work in pixel space and use various saliency measures. The challenge is to adjust the image shape while trying to preserve important content. In this paper weperform image resizing in feature space using the deep layers of a neural network containing rich important semantic information. We directly adjust the imagefeature maps, extracted from a pre-trained classification network, and reconstruct the resized image using neural-network based optimization. This novel approachleverages the hierarchical encoding of the network, and in particular, the high-level discriminative power of its deeper layers, that can recognize semantic regions and objects, thereby allowing maintenance of their aspect ratios. Our use of reconstruction from deep features results in less noticeable artifacts than use of image-space resizing operators. We evaluate our method on benchmarks, compare it to alternative approaches, and demonstrate its strengths on challenging images.

Keywords

reconstructionimage retargetingdeep seam carvingimage resizing

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Computational Visual Media
Volume 7 Issue 4,
December 2021
Pages 453-466
DOI: 10.1007/s41095-021-0216-x
Cite this article:
Danon D, Arar M, Cohen-Or D, et al. Image resizing by reconstruction from deep features. Computational Visual Media, 2021, 7(4): 453-466. https://doi.org/10.1007/s41095-021-0216-x

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Received: 28 January 2021
Accepted: 25 February 2021
Published: 27 April 2021
© The Author(s) 2021

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