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Putting Motion into the Image Retrieval Interface Defining the colors of 3D objects

Putting Motion into the Image Retrieval Interface Defining the colors of 3D objects. Elise Lewis University of North Texas. Overview. Introduction Background Retrieval issues-CBIR Assumptions 2D vs. 3D Study Conclusions Future Research. Introduction. Images are expected

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Putting Motion into the Image Retrieval Interface Defining the colors of 3D objects

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  1. Putting Motion into the Image Retrieval InterfaceDefining the colors of 3D objects Elise Lewis University of North Texas

  2. Overview • Introduction • Background • Retrieval issues-CBIR • Assumptions • 2D vs. 3D • Study • Conclusions • Future Research Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

  3. Introduction • Images are expected • Automated retrieval systems have been implemented for images • 3D objects bring unique challenges to retrieval systems • Methodology is needed to study 3D objects Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

  4. Background • Content-based image retrieval (CBIR) • Automatically extracted • Feature-based query classes • Color space • Histogram • RGB color space • 3D objects • Ability to rotate and zoom • Provides a 360° view of the object Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

  5. Assumptions and previous research • Previous research explores CBIR systems with 2D images • Little research on 3D objects and retrieval systems • Take prior research and test with attributes of 3D objects • Develop a methodology to measure the differences and similarities between 2D and 3D images-Are they the same? Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

  6. Study • How much of a difference occurs in RGB values given different views of an object? • Front view • 6 views (front, rear, top, bottom, left, right) • Software defined views • N=10 • Viewed on web • Courtesy of Arius 3D (www.arius3d.com) • 3 color channels (Red, Green, Blue) Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

  7. Image Views Front* Top Left Right Bottom Rear Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

  8. 3D objects Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

  9. The Histogram Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

  10. Largest Difference in Level Distribution-How much of a color is present? 232.17 108.49 Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

  11. Largest Difference in Level Distribution-Front/Top View Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

  12. Smallest Difference in Level Distribution 121.2 121.1 Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

  13. Smallest Difference in Level Distribution-Front/Rear Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

  14. Largest Difference in Spread-How much of color range is present? 100.002 59.54 Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

  15. Largest Difference in Spread-How much of color range is present? Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

  16. Conclusions • Views change the levels of RGB • Views change the range of color • Complementary views (i.e. top-bottom) do not have same mean or SD • Greatest differences occur between objects with large surface areas versus small surface areas • Depth of detail needs to be defined • How important are the shades of a color? • Information needs of a browser vs. researcher Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

  17. Limitations and Future Research • Use different color space • HSV • L*a*b • More images from different domains • Wide variety of color-Art • Detailed color-Botany • Test algorithms for weighting and combining views and values Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

  18. References • Curtin, D. P., (2003). Editing your images: Understanding Histograms. Retrieved from the Shortcourses Website: http://www.shortcourses.co/editing/edit-14.htm. • Gudivada, V.N., Raghavana, V.V., (1995). Content-Based Image Retrieval Systems. IEEE, 18-23. • Konstantindis, K., Gasteratos, A., and Adndreadis, I., (2005). Image retrieval based on fuzzy color histogram processing. Optics Communications,(248), 4-6, 375-386 • Lee, S. M., Xin, J., H., and Westland, S., (2005).Evaluation of image similarities by histogram intersection. Color Research & Applications, (30), 4, 265- 274 • Reichmann, M., (2005). Understanding Histograms. Retrieved from the Luminous Landscape website: http://www.luminous-landscape.com/tutorials/understandingseries Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

  19. Thank You! • Questions, suggestions or comments? Elise Lewis elewis@unt.edu Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005

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