A Lightweight Image Retrieval System for Paintings

1. A Lightweight Image Retrieval System for Paintings T. Lombardi, S. Cha, and C. Tappert January 19th, 2005

2. Introduction Students of art history learn three primary skills: • Formal analysis • Comparison • Classification How can computer science contribute to the development of these skills? Figure 1: Girl with a Pearl Earring, Jan Vermeer, 1665 Electronic Imaging 2005

3. Working Hypothesis • An Interactive Indexing and Image Retrieval System (IIR) for fine-art paintings can aid students in these endeavors by providing: • a mathematical summarization of an image • a measurable basis for comparing two images • an elementary way to classify an image relative to those in a database Electronic Imaging 2005

4. Previous Work We synthesize the goals of two research areas: • Classification of paintings: • R. Sablatnig, P. Kammerer, and E. Zolda, “Hierarchical Classification of Paintings Using Face- and Brush Stroke Models”, in Proc. of the 14th International Conference on Pattern Recognition (1998). • D. Keren, “Painter Identification Using Local Features and Naïve Bayes”, in Proc. of the 16th International Conference on Pattern Recognition (2002). • Image retrieval which aims to bridge the semantic gap: • J. Corridoni, A. Del Bimbo, and P. Pala, “Retrieval of Paintings using Effects Induced by Color Features”, in Proc. of the International Workshop on Content-Based Access of Image and Video Databases (1998). • Can we construct a feature set that satisfies the objectives of both areas while providing analytically relevant data to students? Electronic Imaging 2005

5. System Overview The system consists of two major components: • Image Database • stores images, thumbnail images, and extracted features for later retrieval and analysis. • Graphical User Interface • provides interactive query capabilities to the end user Electronic Imaging 2005

6. Database Construction • An XML index file stores extracted features and control information. • A file system stores images and thumbnail images. • The open design of the database contributes to the goals of ease of use and exchange of information. Electronic Imaging 2005

7. Database Construction – Cont. Figure 3: File System Figure 2: XML Index File Electronic Imaging 2005

8. Global Feature Extraction Two different kinds of features are extracted: • Palette features • concern the set of colors in an image (color map) • examples: palette scope • Canvas features • concern the spatial and frequency distribution of colors in an image (image index) • examples: max, min, median, mean (for each color channel) Electronic Imaging 2005

9. Sample Feature Set Table 1: Sample Features used for Web Museum Interactive Test Electronic Imaging 2005

10. Example: Palette Scope Figure 4: Hallucinogenic Toreador Salvador Dali, 1970 Figure 5: Composition with Large Blue Plane, Red, Black, Yellow, and Gray Piet Mondrian, 1921 Palette Scope -- the total number of unique colors used in an image. We expect Dali’s piece to have a higher palette depth than Mondrian’s work. Electronic Imaging 2005

11. Example: Palette Scope – Cont. Formal definition of Palette Scope (U): U = C/P Where C=Total # of unique colors measured in RGB or HSV triples. P= Total # of pixels in an image. Electronic Imaging 2005

12. Example: Palette Scope – Cont. Table 2: Palette Scope statistics. We see that Dali uses more of the color spectrum than Mondrian. Palette depth is an important feature for artist and period style identification because many styles are defined by color, i.e. Picasso’s Blue Period and fauvism. Electronic Imaging 2005

13. Graphical User Interface • The GUI consists of three primary windows for: • Analysis • Comparison • Classification Electronic Imaging 2005

14. Analysis Window Figure 6: The Analysis Window Electronic Imaging 2005

15. Comparison Window Figure 7: The Comparison Window Electronic Imaging 2005

16. Classification Window Figure 8: The Classification Window Electronic Imaging 2005

17. Test Results Two types of tests were conducted: • Feature tests • Feature tests focus on the accuracy of specific collections of features. • Interactive tests • Interactive tests assess the accuracy of the system as a whole. Electronic Imaging 2005

18. Feature Test Figure 9: Les Demoiselles d’Avignon, Pablo Picasso, 1907. Figure 10: Road with Cypress and Star, Vincent Van Gogh, 1890. Table 3: Feature test to distinguish the work of Picasso and Van Gogh. Electronic Imaging 2005

19. Initial Interactive Test Database of 10 works of each of the following ten artists: Braque, Cezanne, De Chirico, El Greco, Gauguin, Modigliani, Mondrian, Picasso, Rembrandt, and Van Gogh. Table 4: Initial Interactive Test Electronic Imaging 2005

20. Interactive Test: Web Museum Table 5: Results from Web Museum Interactive Test Electronic Imaging 2005

21. Evaluation ofWeb Museum Test Results • Overall result: 56.3% accuracy • 36.3% better than blind guessing (10 guesses/50 artists = 20%) • Dissecting the classification mistakes reveals some intelligent mistakes • Rembrandt is most often confused with Caravaggio, Ast, and Vermeer Electronic Imaging 2005

22. Conclusions • Simple palette and canvas features are sufficient for an interactive classification system • A single feature set can serve for classification and image retrieval applications • A general feature set can adequately serve for educational applications • Although showing promise, we currently have a low confidence system Electronic Imaging 2005

23. Future Work • Add texture features • Improved color features: hue histograms • Improved distance metrics: modulo comparison of hue histograms • Test against larger datasets Electronic Imaging 2005