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Volume: 29 | Article ID: art00025
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Defining Self-Similarity of Images Using Features Learned by Convolutional Neural Networks
  DOI :  10.2352/ISSN.2470-1173.2017.14.HVEI-142  Published OnlineJanuary 2017
Abstract

In recent years, Convolutional Neural Networks (CNNs) have gained huge popularity among computer vision researchers. In this paper, we investigate how features learned by these networks in a supervised manner can be used to define a measure of self-similarity, an image feature that characterizes many images of natural scenes and patterns, and is also associated with images of artworks. Compared to a previously proposed method for measuring self-similarity based on oriented luminance gradients, our approach has two advantages. Firstly, we fully take color into account, an image feature which is crucial for vision. Secondly, by using higher-layer CNN features, we define a measure of selfsimilarity that relies more on image content than on basic local image features, such as luminance gradients.

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Anselm Brachmann, Christoph Redies, "Defining Self-Similarity of Images Using Features Learned by Convolutional Neural Networksin Proc. IS&T Int’l. Symp. on Electronic Imaging: Human Vision and Electronic Imaging,  2017,  pp 188 - 194,  https://doi.org/10.2352/ISSN.2470-1173.2017.14.HVEI-142

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Copyright © Society for Imaging Science and Technology 2017
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Electronic Imaging
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