TitleTowards automated classification of fine-art painting style
NameArora, Ravneet Singh (author), Elgammal, Ahmed (chair), Pavlovic, Vladimir (co-chair), Kulikowski, Casimir (co-chair), Rutgers University, Graduate School - New Brunswick,
Pattern recognition systems
DescriptionThis thesis presents a comparative study of different classification methodologies for the task of fine-art genre classification. The problem of painting classification involves classifying new unknown paintings among different art genres. Two-level comparative study is performed for this classification problem. The first level reviews the performance of discriminative vs. generative models while the second level touches the features aspect of the paintings and compares Semantic-level features vs low-level and intermediate-level features present in the painting. Three models are studied and compared, namely - 1) A Discriminative model using a Bag-of-Words (BoW) approach; 2) A Generative model using BoW; 3) Discriminative model using Semantic-level features. Various experiments and techniques like Bag of Words model, Topic models and Classeme features are employed to get insights into potential of these automatic classification techniques for painting styles.
NoteIncludes bibliographical references
Noteby Ravneet Singh Arora
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.