The Classification of Style in Painting: Computational Approaches to Artistic Style

In recent years, advances in computer science have contributed to the appreciation, analysis, and understanding of fine art. Researchers have applied new techniques from the fields of digital image processing and machine intelligence to the problem of authenticating works of art. This book demonstrates that these same techniques when applied to artistic style generally allow researchers to develop mathematical models of style in painting. These models in turn provide a basis for such diverse activities as the classification of style, the comparison of artistic styles, and the visualization of style relationships. The study covers both the artistic and technical aspects of such systems including a review of terms used in the formal analysis of art and descriptions of the techniques employed to model these features. The monograph includes an extensive technical discussion of feature extraction and comparison techniques. The author includes a thorough review of the literature and provides an ample list of references for those requiring additional information. The study should help anyone interested in designing a system for the management and analysis of digital images.

Thomas Lombardi earned a doctorate in Computer Science from Pace University and a Master of Arts in History from Fordham University. He currently works as a developer at eScholar LLC., a data warehousing company specializing in educational data. His current research interests include messaging systems and the Council of Constance.