Image style classification based on learnt deep correlation features

Wei Ta Chu, Yi Ling Wu

Research output: Contribution to journalArticlepeer-review

48 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, and we design various correlations and transform them into style vectors, and investigate classification performance brought by different variants. In addition to intralayer correlation, interlayer correlation is proposed as well, and its effectiveness is verified. After showing the effectiveness of deep correlation features, we further propose a learning framework to automatically learn correlations between feature maps. Through extensive experiments on image style classification and artist classification, we demonstrate that the proposed learnt deep correlation features outperform several variants of convolutional neural network features by a large margin, and achieve the state-of-the-art performance.

Original languageEnglish
Article number8279468
Pages (from-to)2491-2502
Number of pages12
JournalIEEE Transactions on Multimedia
Issue number9
Publication statusPublished - 2018 Sep

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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