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Painting classification is a novel subfield of research in computer vision and image processing that focuses on empirically defining a painting's authorship, date of creation, or genre. The applications of the subfield have increased in response to changing art markets and expanding and emerging research. Basic features like color and line in and of themselves prove inadequate in this study. Texture analysis provides one of the promising additional features. In this paper, I specifically investigate two different measures of texture analysis, Gabor filters and Haralick features, in addition to basic features, and their applications in a specific painting classification problem. I also consider different methods of collecting this data, and the impact of each on classification.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Fauber, Jacob, "Feature Extraction and Texture Analysis in the Classification of Paintings" (2014). Senior Projects Fall 2014. 35.