imgs.ai. A Deep Visual Search Engine for Digital Art History

We present a Web application that facilitates the deep visual search of image collections using contemporary machine learning. We discuss image retrieval as a combined computer vision/human-computer interaction problem, and propose that the standardization of feature extraction is one of the main problems that digital art history faces today.

Related Organizations

University of California
United States
University of California, Los Angeles
United States
University of California
United States
University of California, Santa Barbara
United States
Philipps-University of Marburg
Germany

View all View all

Keywords

Paper, image processing and analysis, machine learning, digital art history, Short Presentation, information retrieval and querying algorithms and methods, Interface design, and analysis, artificial intelligence and machine learning, development, Art history, image retrieval, computer vision

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average