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A Perception-Inspired Deep Learning Framework for Predicting Perceptual Texture Similarity

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journal contribution
posted on 05.11.2020, 13:54 by Ying Gao, Yanhai Gan, Lin Qi, Huiyu Zhou, Xinghui Dong, Junyu Dong
Similarity learning plays a fundamental role in the fields of multimedia retrieval and pattern recognition. Prediction of perceptual similarity is a challenging task as in most cases we lack human labeled ground-truth data and robust models to mimic human visual perception. Although in the literature, some studies have been dedicated to similarity learning, they mainly focus on the evaluation of whether or not two images are similar, rather than prediction of perceptual similarity which is consistent with human perception. Inspired by the human visual perception mechanism, we here propose a novel framework in order to predict perceptual similarity between two texture images. Our proposed framework is built on the top of Convolutional Neural Networks (CNNs). The proposed framework considers both powerful features and perceptual characteristics of contours extracted from the images. The similarity value is computed by aggregating resemblances between the corresponding convolutional layer activations of the two texture maps. Experimental results show that the predicted similarity values are consistent with the human-perceived similarity data.

Funding

J. Dong is supported by National Key R&D Program of China (Grant No. 2018AAA0100602) and National Natural Science Foundation of China (NSFC) (Grant No. 41576011, U1706218 and 41927805). (Corresponding author: Junyu Dong). H. Zhou is was supported in part by the U.K. EPSRC under Grant EP/N011074/1, Royal Society-Newton Advanced Fellowship under Grant NA160342, and European Unions Horizon 2020 Research and Innovation Program through the Marie-Sklodowska-Curie under Grant 720325. L. Qi is supported by National Natural Science Foundation of China (NSFC) (Grant No. 61501417).

History

Citation

IEEE Transactions on Circuits and Systems for Video Technology ( Volume: 30, Issue: 10, Oct. 2020)

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Informatics

Version

AM (Accepted Manuscript)

Published in

IEEE Transactions on Circuits and Systems for Video Technology

Volume

30

Issue

10

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

1558-2205

Acceptance date

26/09/2019

Copyright date

2019

Available date

30/09/2019

Notes

The file associated with this record is under embargo until publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.

Language

en