Works of art are salience maps, meaning that when anyone draws (no matter how skilled they might be) the result is a map of the most important visual elements needed to understand a scene. Without this artists (skill notwithstanding) would not be able to draw at all, instead they would record all parts of the scene without discrimination - which is exactly what photographs do. Moreover, the salience of a visual element is not an inherent property of the object, rather salience depends on all other objects in the scene - it is a relative, non-local property.
This page shows results of painting using filters, in which small patches of an image are replaced with a mark that affects a paint-brush stroke. This filtering approach is the basis of much of nonphotorealistic rendering from photographs, but we were first to recognise and use global salience as a control. We went on to build a filtering system that not only includes global salience but which can be trained by users to recognise particular patterns (user selected) such as corners and edges. Finally this was integrated in the first system that used genetic search to determine where to lay down strokes.
J. Collomosse and P. Hall, “Painterly rendering using image salience”, Eurographics UK, 122-128, 2002.
P.Hall and M. Owen, “A trainable low-level feature detector”, International Conference on Pattern Recognition, 708-711, 2004.
J. Collomosse and P. Hall, “Genetic Paint: A Search for Salient Paintings”, Lecture Notes in Computer Science (Proc. EvoMUSART), vol. 3449, pp. 437-447, 2005.