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Classification of Artistic Styles Using Binarized Features Derived from a Deep Neural Network

  • Yaniv BarEmail author
  • Noga Levy
  • Lior Wolf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8925)

Abstract

With the vast expansion of digital contemporary painting collections, automatic theme stylization has grown in demand in both academic and commercial fields. The recent interest in deep neural networks has provided powerful visual features that achieve state-of-the-art results in various visual classification tasks. In this work, we examine the perceptiveness of these features in identifying artistic styles in paintings, and suggest a compact binary representation of the paintings. Combined with the PiCoDes descriptors, these features show excellent classification results on a large scale collection of paintings.

Keywords

Local Binary Pattern Convolutional Neural Network Deep Neural Network Feature Fusion Binarized Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.The Blavatnik School of Computer ScienceTel Aviv UniversityTel Aviv-YafoIsrael

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