By Shlomo Dubnov, Kevin Burns, Yasushi Kiyoki
The skill to speak cultural codes in multimedia will depend on their that means and wonder, as perceived via diverse audiences worldwide. during this publication, the continued examine on computational modeling of visible, musical and textual contents is defined by way of settling on and mapping their semantic representations throughout varied cultures. The underlying psychology of sense-making is quantified via research of aesthetics by way of organizational and structural features of the contents that impression an audience’s formation of expectancies for destiny signs, violations of those expectancies, and reasons in their that means. Complexity-accuracy tradeoffs in sound illustration are additional used to improve new computational equipment that trap poietic and aesthetic points in song communique. Experimental reports are suggested that attempt to symbolize personal tastes for complexity in summary, classical and conventional artwork and song throughout samples of Western and much japanese cultures. those experiments illustrate how aesthetics may be computed when it comes to semantic and knowledge measures, highlighting commonalities and uncovering variations in aesthetic personal tastes throughout cultures and individuals.
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Extra resources for Cross-Cultural Multimedia Computing: Semantic and Aesthetic Modeling
J Math Arts 6:65–75 33. Oatley K (2003) Creative expression and communication of emotions in the visual and narrative arts. In: Davidson R, Scherer K, Goldsmith H (eds) Handbook of affective sciences. Oxford University Press, Oxford, pp 481–502 34. Parsons M (1987) How we understand art: a cognitive developmental account of aesthetic experience. Cambridge University Press, Cambridge 35. Phillips F, Norman J, Beers A (2010) Fechner’s aesthetics revisited. Seeing and Perceiving 23:263–271 36. Reichhold J (2002) Writing and enjoying haiku: a hands-on guide.
And once again, the meaning assigned to signals comes not from signals contained within the artwork itself—but rather from common knowledge shared by a culture of artists and audiences who live in the same natural world. In today’s state of the art, it remains an unsolved challenge for artiﬁcial intelligence to model the meanings that humans assign to signals as they experience artworks. In language if not other media, this requires rich representations of human knowledge about the real world—including hypothetical meanings of evidential signals, along with prior probabilities of the form P(meaning) and conditional probabilities of the form P(signal|meaning).
A novel aspect of EVE’s approach to these artworks lies in modeling Y as a visual entropy VE. Details are discussed in , but the main point here is that VE measures spatial complexity in a manner that differs from classical entropy H. 3 An Experimental Study of Visual Aesthetics 31 An represents the area of an individual white shape computed as a fraction of the total area. Thus per this VE, total surprisal for an artwork is computed as the average surprisal across all white shapes (bounded by black lines), with surprisal for each white shape given by −log An.
Cross-Cultural Multimedia Computing: Semantic and Aesthetic Modeling by Shlomo Dubnov, Kevin Burns, Yasushi Kiyoki