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Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-8
Kibum Jin , Soongsil University Computer Institute, Dongjak Gu, Sangdo-Dong 511, Seoul, Korea
Latifur Khan , University of Texas Dallas, Data Mining Lab., Richardson, 75083-0688, USA
Yohan Jin , University of Texas Dallas, Multimedia Systems Lab., Richardson, 75083-0688, USA
Recently, images on the Web and personal computers are prevalent around the human’s life. To retrieve effectively those images, there are many AIA (Automatic Image Annotation) algorithms. However, it still suffers from low-level accuracy since it couldn’t overcome the semantic-gap be tween low-level features (‘color’,‘texture’ and ‘shape’) and high-level semantic meanings (e.g., ‘sky’,‘beach’). Namely, AIA techniques annotates images with many noisy key words. Refinement process has been appeared in these days and it tries to remove noisy keywords by using Knowledge-base and boosting candidate keywords. Because of limitless of candidate keywords and the incorrectness of web-image textual descriptions, this is the time we need to have deterministic polynomial time algorithm. We show that finding optimal solution for removing noisy keywords in the graph is NP-Complete problem and propose new methodology for KBIAR (Knowledge Based Image Annotation Refinement) using the randomized approximation graph algorithm as the general deterministic polynomial time algorithm.
Kibum Jin, Latifur Khan, Yohan Jin, "The randomized approximating graph algorithm for image annotation refinement problem", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-8, doi:10.1109/CVPRW.2008.4563044
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