As mentioned above, even standard recognition tasks of differentiation among natural classes of objects could encounter significant difficulties. However, the developing field of computerized visual art analysis is facing much greater challenges. Indeed, one of the considered tasks is not to identify objects in the painting and determine their relevance to the known classes, but rather to capture much less obvious and hard-to-quantify features that allow relating considered piece of art to a particular famous painter, school of visual art or epoch. Even the same object or scene can be represented in a wide variety of styles by different painters or schools of art. Thus, variability in such problems could create even more serious challenges than in more standard problems of generic category (class) recognition. Similar to more developed areas of computer vision, certain problems of computerized visual art analysis are more formalizable and quantifiable which warranties certain level of success. Mainly, these areas include applications dealing with the analysis of certain aspect of style used by artist. For example, fractal analysis was shown to be successful in identifying paintings of Jackson Pollock. Quantitative characterization of brushstrokes in paintings by van Gogh has been shown to be useful for detection of forgeries. Other computer-based systems for analysis of perspective, shading, color and form have also been used for identification of artists and styles. However, unique style signatures of talented artists cannot be fully captured by a single feature. Typically, personal style of an artist consists of complex combination of features. There is no universal way for optimal choice of such features and for their quantification. A related hard-to-formalize problem is quantitative differentiation between “high art”, that is capable of capturing viewer attention on deep psycho-emotional level, and mediocre visual representation of the same scene that lacks such “magical” effect. Intuitively, potential quantitative measures capable to identify such subtle effects of “high art” should be based on optimal combination and interrelation of many different features that characterize style of the true artist. Such multi-feature measures could be very useful in providing objective quantitative support and interpretation for opinions expressed by art experts who may have intuitive ability to grasp complex multi-level visual information implicitly encoded in the art work. More importantly, such multi-expert quantitative measures could consistently discover effects which could be difficult or impossible to notice even for the most experienced human experts. Computerized system with these capabilities could be especially useful in applications requiring fast processing of large number of images where abilities of human experts are significantly inferior to those of automated systems.