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.
- Image recognition
- neural networks
- visual art recognition
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van den Herik, H.J., Postma, E.O. (2000). Discovering the Visual Signature of Painters. In: Kasabov, N. (eds) Future Directions for Intelligent Systems and Information Sciences. Studies in Fuzziness and Soft Computing, vol 45. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1856-7_7
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