@inproceedings{1502e29c4d294735a9de9454558131e1,
title = "Classification of artistic styles using binarized features derived from a deep neural network",
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.",
author = "Yaniv Bar and Noga Levy and Lior Wolf",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 13th European Conference on Computer Vision, ECCV 2014 ; Conference date: 06-09-2014 Through 12-09-2014",
year = "2015",
doi = "10.1007/978-3-319-16178-5_5",
language = "الإنجليزيّة",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "71--84",
editor = "Bronstein, {Michael M.} and Carsten Rother and Lourdes Agapito",
booktitle = "Computer Vision - ECCV 2014 Workshops, Proceedings",
address = "ألمانيا",
}