• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Inna Vasilyeva
Analysis Machine Learning techniques to artistic movement recognition
Data Mining
(Master’s programme)
2018
The proposed methods for solving the problem of recognizing the style of the visual arts were tested on the Pandora dataset and included the construction of a classifier over the features obtained by classic methods of computer vision, as well as over the features obtained from the inner layers of state-of-the-art neural networks. Among all the proposed methods, the classifier based on logistic regression in combination with the internal features of the GoogLeNet v3 network provided best results. Also, the data classes were visualized in the context of the neural network output.

The proposed classifier, using the features of classic computer vision, is competitive against the background of other classifiers using similar features. The use of pre-trained GoogLeNet and logistic regression gave the best result among the published works, and the proposed visualization provided new insights for the data.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses