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

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Presentation on theme: "Putting Motion into the Image Retrieval Interface Defining the colors of 3D objects Elise Lewis University of North Texas."— Presentation transcript:

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

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

3 Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005 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

4 Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005 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

5 Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005 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?

6 Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005 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)

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

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

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

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

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

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

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

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

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

16 Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005 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

17 Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005 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

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

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


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