|dc.description.abstract||This thesis introduces a framework for analyzing the underlying visual similarities among art images. Three types of art related images are investigated. They are: (1) painterly rendered images which are ``painting-like" images generated by painterly rendering algorithms; (2) painting images which are digitized copies of paintings; (3) graphic design images which are graphic art used in various logos, trademarks and symbols. However, the main focus of this thesis is given to the later two types of ``real" art images: \ie painting and graphic design images.
In the proposed framework, image features used for analyzing art images are defined to ``translate" qualitative art concepts or principles into quantitative numerical form. And then a series of Self-Organizing Map (SOM) based methods are introduced to discover, analyze and visualize the underlying visual similarities of art images. Furthermore, two Self-Organizing Map Best Matching Unit Entropy (SOM-BMU Entropy) based approaches are proposed to compare and visualize the data similarity of multiple sets of images. Based on the proposed framework, applications such as reverse painterly rendering, painting artistic influence analysis and logo similarity retrieval are also presented.
Different from previous art imaging systems, the proposed framework aims to bridge the gaps between artistic concepts, image features and numeric solutions, which allows the numeric analysis results to be explained by art concepts and to be visualize easily. Therefore, using the proposed framework, art image data can be better organized and explored; visual similarities of art images can be better understood and explained.||en_AU|
|dc.publisher||University of Sydney||en_AU|
|dc.publisher||Faculty of Engineering & IT||en_AU|
|dc.publisher||School of Information Technology||en_AU|
|dc.rights||The author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.||en_AU|
|dc.subject||Visual similarity analysis||en_AU|
|dc.subject||Art image data||en_AU|
|dc.subject||Image feature extraction||en_AU|
|dc.subject||Self-organizing map (SOM)||en_AU|
|dc.title||On the visual similarity analysis and visualization of art image data||en_AU|
|dc.type.pubtype||Doctor of Philosophy Ph.D.||en_AU|
|dc.description.disclaimer||Access is restricted to staff and students of the University of Sydney . UniKey credentials are required. Non university access may be obtained by visiting the University of Sydney Library.||en_AU|
|Appears in Collections:||Sydney Digital Theses (University of Sydney Access only)|