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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
The randomized approximating graph algorithm for image annotation refinement problem
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Yohan Jin, University of Texas Dallas, Multimedia Systems Lab., Richardson, 75083-0688, USA
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
B. Prabhakaran, 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.
Citation:
Yohan Jin, Kibum Jin, Latifur Khan, B. Prabhakaran, "The randomized approximating graph algorithm for image annotation refinement problem," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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