Painter Identification Using Local Features and Naive Bayes

The goal of this paper is to offer a framework for image classification "by type". For example, one may want to classify an image of a certain office as man-made ? as opposed to outdoor ? scene, even if no image of a similar office exists in the training set. This is accomplished by using local features, and using the naive Bayes classifier. The application presented here is classification of paintings; after the system is presented with a sample of paintings of various artists, it tries to determine who was the painter who painted it. The result is local ? each small image block is assigned a painter, and a majority vote determines the painter. The results are roughly visually consistent with human perception of various artists' style.

• Type: Conference
• Year: 2002
• Where: ICPR
• Authors: Daniel Keren
• Keywords: Computer Vision, ICPR 2002, Similar Office Exists, Small Image Block, Various Artists