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To facilitate computer analysis of visual art, in the form of paintings, weintroduce Pandora (Paintings Dataset for Recognizing the Art movement)database, a collection of digitized paintings labelled with respect to theartistic movement. Noting that the set of databases available as benchmarks forevaluation is highly reduced and most existing ones are limited in variabilityand number of images, we propose a novel large scale dataset of digitalpaintings. The database consists of more than 7700 images from 12 artmovements. Each genre is illustrated by a number of images varying from 250 tonearly 1000. We investigate how local and global features and classificationsystems are able to recognize the art movement. Our experimental resultssuggest that accurate recognition is achievable by a combination of variouscategories.To facilitate computer analysis of visual art, in the form ofpaintings, we introduce Pandora (Paintings Dataset for Recognizing the Artmovement) database, a collection of digitized paintings labelled with respectto the artistic movement. Noting that the set of databases available asbenchmarks for evaluation is highly reduced and most existing ones are limitedin variability and number of images, we propose a novel large scale dataset ofdigital paintings. The database consists of more than 7700 images from 12 artmovements. Each genre is illustrated by a number of images varying from 250 tonearly 1000. We investigate how local and global features and classificationsystems are able to recognize the art movement. Our experimental resultssuggest that accurate recognition is achievable by a combination of variouscategories.

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