Predicting and Grouping Digitized Paintings by Style using Unsupervised Feature Learning.

ABSTRACT:

Objective

To create an system to aid in the analysis of art history by classifying and grouping digitized paintings based on stylistic features automatically learned without prior knowledge.

Material and methods

6,776 digitized paintings from 8 different artistic styles (Art Nouveau, Baroque, Expressionism, Impressionism, Realism, Romanticism, Renaissance, and Post-Impressionism) were utilized to classify (predict) and cluster (group) paintings according to style. The method of unsupervised feature learning with K-means (UFLK), inspired by deep learning, was utilized to extract features from the paintings. These features were then used in: (1) a support vector machine algorithm to classify the style of new test paintings based on a training set of paintings having known styl

SUBMITTER: Gultepe E 

PROVIDER: S-EPMC6051702 | biostudies-literature | 2018 May-Jun

REPOSITORIES: biostudies-literature