Genre and Style Based Painting Classification

Abstract

As the size of digitized painting collections increase, it becomes more difficult to organize and retrieve paintings from these collections. To manage search and other similar operations efficiently, it becomes necessary to organize the painting databases into classes and sub-classes. Manual tagging of these ever-increasing databases would become very costly and time consuming. The above challenging problem has motivated researchers to work in the area of painting analysis, genre and style classification, artist classification and automatic annotation of paintings with these tags. These problems are quite difficult as first the expected human performance for this task for non-expert but reasonably knowledgeable individuals is believed to be well below 100% percent. And second, there is a very big databases of paintings with relatively few painters painting in a single genre and style and many who paint in multiple genres and styles. In this paper, we explore the problem of feature extraction on the paintings and focus on classification of paintings into their genres and styles. We worked with 6 genres and 10 styles. We get an accuracy of 84.56% for genre classification. We achieved an accuracy of 62.37% for classifying the paintings into 10 styles. We include a comparison to existing feature extraction and classification methods as well as an analysis of our own approach across different feature vectors.