putting motion into the image retrieval interface defining the colors of 3d objects elise lewis...
TRANSCRIPT
Putting Motion into the Image Retrieval Interface
Defining the colors of 3D objects
Elise Lewis
University of North Texas
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
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
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
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?
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)
Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005
Image ViewsFront*
Rear
Top
Bottom
Left
Right
Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005
3D objects
Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005
The Histogram
Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005
Largest Difference in Level Distribution-How much of a color is present?
Butterfly
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Mean SD
Le
ve
ls
Front Rear Top Bottom Left RightFront Rear Top Bottom Left RightFront Rear Top Bottom Left RightFront Rear Top Bottom Left Right
232.17
108.49
Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005
Largest Difference in Level Distribution-Front/Top View
Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005
Smallest Difference in Level Distribution
Cunieform
0153045607590
105120135150165180195210225240255
Mean SD
Le
ve
ls
Front Rear Top Bottom Left RightFront Rear Top Bottom Left RightFront Rear Top Bottom Left RightFront Rear Top Bottom Left Right
121.1
121.2
Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005
Smallest Difference in Level Distribution-Front/Rear
Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005
Largest Difference in Spread-How much of color range is present?
Arrow
0153045607590
105120135150165180195210225240255
Mean SD
Levels
Front Rear Top Bottom Left RightFront Rear Top Bottom Left RightFront Rear Top Bottom Left RightFront Rear Top Bottom Left Right
100.002
59.54
Putting Motion into the Image Retrieval Interface ASIS&T Annual Conference2005
Largest Difference in Spread-How much of color range is present?
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
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
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: 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
Thank You!
Questions, suggestions or comments?
Elise Lewis