Discovering the Visual Signature of Painters

Abstract

Recent developments in image classification have focused on efficient preprocessing of visual data to improve the performances of neural networks and other learning algorithms when dealing with content-based classification tasks. Given the high dimensionality and redundancy of visual data, the primary goal of preprocessing is to transfer the original data to a low-dimensional representation that preserves the information relevant for the classification. This contribution reviews modern preprocessing (dimension-reduction) techniques and discusses their advantages and disadvantages. The performance of the techniques is assessed on a difficult painting-classification task that requires painter-specific features to be retained in the low-dimensional representation. Evaluation of the results shows that domain-specific knowledge provides a rough albeit indispensable guideline for determining the appropriate type of preprocessing. Furthermore, the evaluation shows that neural-network techniques are most suitable for executing and fine-tuning the preprocessing and subsequent classification. It is argued that further improvements can be gained by the use of a content-based attentional selection procedure. Our conclusion is that preprocessing should be tailored to the task at hand by combining domain knowledge with neural-network techniques, and that within fifty years the visual signature of painters is as recognizable as is any handwritten signature.