Image style recognition using neural networks

Daniel Kvak / TIM stand

Technological progress makes it possible to digitize an increasing number of works of art. It is no wonder, then, that the automatic classification, indexing and retrieval of fine art images has received increased attention in recent years. However, compared to the classification of natural objects, the classification of works of art is a much more challenging task. Convolutional neural networks, which consist of a number of layers of convolutional matrix operations, are an ideal solution for image analysis and object identification. They use a similar concept as the filters used in graphics editors, but in a more complex and computationally demanding way.

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