Deep correlation features for image style classification

Wei Ta Chu, Yi Ling Wu

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

33 Citations (Scopus)


This paper presents a comprehensive study of deep correlation features on image style classification. Inspired by that correlation between feature maps can effectively describe image texture, we design and transform various such correlations into style vectors, and investigate classification performance brought by different variants. In addition to intra-layer correlation, we also propose inter-layer correlation and verify its benefit. Through extensive experiments on image style classification and artist classification, we demonstrate that the proposed style vectors significantly outperforms CNN features coming from fully-connected layers, as well as outperforms the state-of-the-art deep representation.

Original languageEnglish
Title of host publicationMM 2016 - Proceedings of the 2016 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Number of pages5
ISBN (Electronic)9781450336031
Publication statusPublished - 2016 Oct 1
Event24th ACM Multimedia Conference, MM 2016 - Amsterdam, United Kingdom
Duration: 2016 Oct 152016 Oct 19

Publication series

NameMM 2016 - Proceedings of the 2016 ACM Multimedia Conference


Conference24th ACM Multimedia Conference, MM 2016
Country/TerritoryUnited Kingdom

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Software


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