id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_l376ydartvbwxltcusr7kjzohu	Fahad Shahbaz Khan	Painting-91: a large scale database for computational painting categorization	2014	14	.pdf	application/pdf	8772	839	60	For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. The task is to automatically categorize an image to its artist and style. datasets, the large number of images with a wide variety of categories and standard evaluation protocols, both local and global features, popular in image classification, for computational painting categorization. As a possible application of this data, we evaluate existing saliency methods on painting images. – We introduce a new large scale dataset of 4266 painting images from 91 different painters. investigate the state-of-the-art object recognition methods for the task of painting and style classification. used for artist and style classification, this dataset contains local and global annotations for visual themes. artistic style dataset consists of 513 paintings of 9 different painters. The second row in Table 3 shows the results obtained using different visual features for style categorization.	./cache/work_l376ydartvbwxltcusr7kjzohu.pdf	./txt/work_l376ydartvbwxltcusr7kjzohu.txt