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Evaluation strategies for image understanding and retrieval
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Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval table of contents
Hilton, Singapore
SESSION: Special session 2: multimedia information retrieval: challenges and real-world applications table of contents
Pages: 217 - 226  
Year of Publication: 2005
ISBN:1-59593-244-5
Authors
Keiji Yanai  University of Electro-Communications, Tokyo, Japan
Nikhil V. Shirahatti  University of Arizona, Tucson, AZ
Prasad Gabbur  University of Arizona, Tucson, AZ
Kobus Barnard  University of Arizona, Tucson, AZ
Sponsors
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
ACM: Association for Computing Machinery
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
Publisher
ACM  New York, NY, USA
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ABSTRACT

We address evaluation of image understanding and retrieval large scale image data in the context of three evaluation projects. The first project is a comprehensive strategy for evaluating image retrieval algorithms and provides an open reference data set for doing so. The second project develops word prediction as a semantically relevant evaluation strategy, and applies it to the evaluation of of image processing methods for semantic image analysis. The third project evaluates words for suitability of their visual properties for use in an image annotation framework.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Keiji Yanai: colleagues
Nikhil V. Shirahatti: colleagues
Prasad Gabbur: colleagues
Kobus Barnard: colleagues