title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ð¹ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð² Ð·Ð°Ð´Ð°ÑÐµ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÐºÐ°ÑÑÐ¸Ð½
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Research of deep learning applications in the problem of art painting recognition
Creators: name: ÐÐ°Ð»Ð¸ÑÐ»Ð»Ð¸Ð½, ÐÐ»ÐµÐºÑÐ°Ð½Ð´Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2019
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐÐ°ÑÐ¸Ð½Ð½Ð°Ñ Ð³ÑÐ°ÑÐ¸ÐºÐ°
Ð Ð°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¾Ð±ÑÐ°Ð·Ð¾Ð²
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
Ð´Ð¾Ð¿Ð¾Ð»Ð½ÐµÐ½Ð½Ð°Ñ ÑÐµÐ°Ð»ÑÐ½Ð¾ÑÑÑ
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐµÐ¼Ð°Ð½ÑÐ¸ÑÐµÑÐºÐ°Ñ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ
Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ð¹

description: Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÑÑÑÑ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð² ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ð¸ ÑÐ¸ÑÑÐµÐ¼Ñ Ð´Ð¾Ð¿Ð¾Ð»Ð½ÐµÐ½Ð½Ð¾Ð¹ ÑÐµÐ°Ð»ÑÐ½Ð¾ÑÑÐ¸. ÐÐ°Ð½Ð½Ð°Ñ ÑÐ¸ÑÑÐµÐ¼Ð° Ð¿Ð¾Ð·Ð²Ð¾Ð»Ð¸Ñ Ð¿Ð¾ÑÐµÑÐ¸ÑÐµÐ»ÑÐ¼ Ð¼ÑÐ·ÐµÐµÐ² Ð¿Ð¾Ð»ÑÑÐ¸ÑÑ Ð²ÑÑ Ð¸Ð½ÑÐµÑÐµÑÑÑÑÑÑ Ð¸Ñ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ Ð¾Ð± ÑÐºÑÐ¿Ð¾Ð½Ð°ÑÐ°Ñ ÑÐ°Ð¼Ð¾ÑÑÐ¾ÑÑÐµÐ»ÑÐ½Ð¾ Ð¸ Ð² ÑÐ´Ð¾Ð±Ð½Ð¾Ð¼ Ð²Ð¸Ð´Ðµ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÐ¼Ð°ÑÑÑÐ¾Ð½Ð°. Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ: ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ðµ ÑÐ¸ÑÑÐµÐ¼Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÐºÐ°ÑÑÐ¸Ð½Ñ Ð¿Ð¾ ÐµÑ ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸Ð¸ Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÐµÐ¼ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÐ»Ñ Ð´Ð¾ÑÑÐ¸Ð¶ÐµÐ½Ð¸Ñ ÑÐµÐ»Ð¸ Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼Ð¾ ÑÐµÑÐ¸ÑÑ Ð´Ð²Ðµ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ: Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ ÐºÐ°ÑÑÐ¸Ð½Ñ Ð¸Ð· ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸Ð¸ Ð¸ ÐµÑ Ð¸Ð´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ. ÐÐµÑÐ²Ð°Ñ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ð° ÑÐµÑÐ°ÐµÑÑÑ Ð¾Ð±ÑÑÐµÐ½Ð¸ÐµÐ¼ Ð¿Ð¾Ð»Ð½Ð¾ÑÑÑÑ ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÑÐµÐ¼Ð°Ð½ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ. ÐÐ¾Ð»ÑÑÐµÐ½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¾ÑÐµÐ½Ñ ÑÐ¾ÑÐ½Ð¾ Ð²ÑÐ´ÐµÐ»ÑÐµÑ ÐºÐ°ÑÑÐ¸Ð½Ñ Ð½Ð° ÑÐ¾ÑÐ¾Ð³ÑÐ°ÑÐ¸Ð¸ Ð¸ Ð¼Ð¾Ð¶ÐµÑ ÑÐ°Ð±Ð¾ÑÐ°ÑÑ Ð½Ð° Ð¾Ð±ÑÐµÐ´Ð¾ÑÑÑÐ¿Ð½ÑÑ Ð¼Ð¾Ð±Ð¸Ð»ÑÐ½ÑÑ ÑÑÑÑÐ¾Ð¹ÑÑÐ²Ð°Ñ, ÑÐ°ÐºÐ¸Ñ ÐºÐ°Ðº ÑÐ¼Ð°ÑÑÑÐ¾Ð½ Ð¸ Ð¿Ð»Ð°Ð½ÑÐµÑ. ÐÑÐ¾ÑÐ°Ñ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ð° ÑÐµÑÐ°ÐµÑÑÑ Ð¾Ð±ÑÑÐµÐ½Ð¸ÐµÐ¼ ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð¸ ÐºÐ¾Ð¼Ð¿Ð°ÐºÑÐ½Ð¾Ð³Ð¾ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ. ÐÐ´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ ÐºÐ°ÑÑÐ¸Ð½Ñ Ð¿Ð¾ Ð´Ð°Ð½Ð½ÑÐ¼ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸ÑÐ¼ ÑÑÐµÐ±ÑÐµÑ Ð¼Ð°Ð»Ð¾ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÑ ÑÐµÑÑÑÑÐ¾Ð² Ð¸, ÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½Ð¾, Ð¼Ð¾Ð¶ÐµÑ ÑÐ°Ð±Ð¾ÑÐ°ÑÑ Ð½ÐµÐ¿Ð¾ÑÑÐµÐ´ÑÑÐ²ÐµÐ½Ð½Ð¾ Ð½Ð° Ð¼Ð¾Ð±Ð¸Ð»ÑÐ½ÑÑ ÑÑÑÑÐ¾Ð¹ÑÑÐ²Ð°Ñ. Ð¡Ð¸ÑÑÐµÐ¼Ð° Ð¿ÑÐ°Ð²Ð¸Ð»ÑÐ½Ð¾ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°ÐµÑ Ð¾ÐºÐ¾Ð»Ð¾ 61% ÐºÐ°ÑÑÐ¸Ð½ Ð½Ð° Ð´Ð°ÑÐ°ÑÐµÑÐµ ÐºÐ°ÑÑÐ¸Ð½ Ð¸Ð· Ð­ÑÐ¼Ð¸ÑÐ°Ð¶Ð°, Ð²ÑÐ´Ð°Ð²Ð°Ñ Ð´Ð»Ñ Ð½ÐµÐ¿ÑÐ°Ð²Ð¸Ð»ÑÐ½Ð¾ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð½Ð½ÑÑ ÑÐ»ÑÑÐ°ÐµÐ² ÐºÐ°ÑÑÐ¸Ð½Ñ ÑÐ¾Ð³Ð¾ Ð¶Ðµ Ð°Ð²ÑÐ¾ÑÐ°, Ð¶Ð°Ð½ÑÐ° Ð¸ ÑÑÐ¸Ð»Ñ, ÑÑÐ¾ ÑÐ°ÐºÐ¶Ðµ ÑÐ²Ð»ÑÐµÑÑÑ Ð¿Ð¾Ð»Ð¾Ð¶Ð¸ÑÐµÐ»ÑÐ½ÑÐ¼ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð¼. Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ ÐºÐ°Ðº Ð¸ Ð¼ÐµÑÐ¾Ð´Ñ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ (Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ¸ PyTorch), ÑÐ°Ðº Ð¸ Ð¼ÐµÑÐ¾Ð´Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð³Ð¾ Ð·ÑÐµÐ½Ð¸Ñ (Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐº OpenCV Ð¸ SkImage).
description: In this work we consider the possibility of using deep learning in the creation of a museum augmented reality system. This system will allow museumâs visitors to get all information they want about the exhibits on their own and in desired form using their smartphones. The aim of this work is to create a painting recognition system using deep learning algorithms. To achieve this goal, it is necessary to solve two problems: extraction of a painting from itâs photo and painting identification. The first problem is solved by training a fully convolutional neural network for the problem of semantic image segmentation. The resulting model very accurately extracts painting from photo and can work on widely available mobile devices such as smartphones and tablets. The second problem is solved by training a convolution neural network to generate a compact representation of the image. Identifying painting on the photo using this representations requires little computing power and thus can work directly on mobile devices. The system correctly recognizes about 61% of the paintings in Hermitage paintings dataset. In incorrect cases system identifies painting as other painting of the same author, genre and style. This is also a good result. In this work we used both deep learning methods (using the PyTorch library) and classical computer vision methods (using the OpenCV and SkImage libraries).