Perceptual texture similarity learning using deep neural networks

Ying Gao, Yanhai Gan, Junyu Dong, Lin Qi, Huiyu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The majority of studies on texture analysis focus on classification and generation, and few works concern perceptual similarity between textures, which is one of the fundamental problems in the field of texture analysis. Previous methods for perceptual similarity learning were mainly assisted by psychophysical experiments and computational feature extraction. However, the calculated similarity matrix is always seriously biased from human observation. In this paper, we propose a novel method for similarity prediction, which is based on convolutional neural networks (CNNs) and stacked sparse auto-encoder (SSAE). The experimental results show that the predicted similarity matrixes are more perceptually consistent with psychophysical experiments compared to other predicting methods.

Original languageEnglish
Title of host publicationICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
EditorsLiang Zhao, Lipo Wang, Guoyong Cai, Kenli Li, Yong Liu, Guoqing Xiao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages856-860
Number of pages5
ISBN (Electronic)9781538621653
DOIs
Publication statusPublished - 25 Jun 2018
Event13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017 - Guilin, Guangxi, China
Duration: 29 Jul 201731 Jul 2017

Publication series

NameICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

Conference

Conference13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017
CountryChina
CityGuilin, Guangxi
Period29/07/201731/07/2017

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported by National Natural Science Foundation of China(NSFC)(No.61271405).

Publisher Copyright:
© 2017 IEEE.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Logic
  • Modelling and Simulation
  • Statistics and Probability

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