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Artistic Photo Filtering Recognition Using CNNs

  • Simone Bianco
  • Claudio Cusano
  • Raimondo Schettini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10213)

Abstract

In this paper we propose an approach based on deep Convolutional Neural Networks (CNNs) to recognize artistic photo filters applied to images. A total of 22 types of Instagram-like filters is considered. Different CNN architectures taken from the image recognition literature are compared on a dataset of more than 0.46 M images from the Places-205 dataset. Experimental results show that not only it is possible to reliably determine whether or not one of these filters has been applied, but also which one. Differently from other tasks, where the fine-tuning of a CNN trained on a different problem is usually good enough, here the fine-tuned AlexNet obtains an accuracy of only 67.5%. We show, instead, that an accuracy of about 99.0% can be obtained by training a CNN from scratch for this specific problem.

Keywords

Confusion Matrix Convolutional Neural Network Convolutional Layer Deep Convolutional Neural Network Computer Vision Task 
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 AG 2017

Authors and Affiliations

  1. 1.Department of Informatics, Systems and CommunicationUniversity of Milano-BicoccaMilanoItaly
  2. 2.Department of Electrical, Computer and Biomedical EngineeringUniversity of PaviaPaviaItaly

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