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ContextNet: representation and exploration for painting classification and retrieval in context
2019
International Journal of Multimedia Information Retrieval
In automatic art analysis, models that besides the visual elements of an artwork represent the relationships between the different artistic attributes could be very informative. Those kinds of relationships, however, usually appear in a very subtle way, being extremely difficult to detect with standard convolutional neural networks. In this work, we propose to capture contextual artistic information from fine-art paintings with a specific ContextNet network. As context can be obtained from
doi:10.1007/s13735-019-00189-4
fatcat:hek35v7hlrevvp354advr76rf4