Pandora: Description of a Painting Database for Art Movement Recognition with Baselines and Perspectives

TL;DR

This paper introduces Pandora, a large-scale, labeled painting dataset with over 7700 images across 12 art movements, and evaluates methods for art movement recognition using local and global features.

Contribution

The paper presents Pandora, a new extensive painting dataset for art movement recognition, addressing limitations of existing benchmarks and providing baseline classification results.

Findings

Accurate art movement recognition is possible with combined feature categories.

Pandora dataset contains over 7700 images from 12 art movements.

Baseline classification systems demonstrate promising results.

Abstract

To facilitate computer analysis of visual art, in the form of paintings, we introduce Pandora (Paintings Dataset for Recognizing the Art movement) database, a collection of digitized paintings labelled with respect to the artistic movement. Noting that the set of databases available as benchmarks for evaluation is highly reduced and most existing ones are limited in variability and number of images, we propose a novel large scale dataset of digital paintings. The database consists of more than 7700 images from 12 art movements. Each genre is illustrated by a number of images varying from 250 to nearly 1000. We investigate how local and global features and classification systems are able to recognize the art movement. Our experimental results suggest that accurate recognition is achievable by a combination of various categories.To facilitate computer analysis of visual art, in the form of paintings, we introduce Pandora (Paintings Dataset for Recognizing the Art movement) database, a collection of digitized paintings labelled with respect to the artistic movement. Noting that the set of databases available as benchmarks for evaluation is highly reduced and most existing ones are limited in variability and number of images, we propose a novel large scale dataset of digital paintings. The database consists of more than 7700 images from 12 art movements. Each genre is illustrated by a number of images varying from 250 to nearly 1000. We investigate how local and global features and classification systems are able to recognize the art movement. Our experimental results suggest that accurate recognition is achievable by a combination of various categories.