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Proceedings Paper

Image understanding algorithms for segmentation evaluation and region-of-interest identification using Bayesian networks
Author(s): Mustafa Jaber; Eli Saber
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Paper Abstract

A two-fold image understanding algorithm based on Bayesian networks is introduced. The methodology has modules for image segmentation evaluation and region of interest (ROI) identification. The former uses a set of segmentation maps (SMs) of a target image to identify the optimal one. These SMs could be generated from the same segmentation algorithm at different thresholds or from different segmentation techniques. Global and regional low-level image features are extracted from the optimal SM and used along with the original image to identify the ROI. The proposed algorithm was tested on a set of 4000 color images that are publicly available and compared favorably to the state-of-the-art techniques. Applications of the proposed framework include image compression, image summarization, mobile phone imagery, digital photo cropping, and image thumb-nailing.

Paper Details

Date Published: 3 June 2011
PDF: 9 pages
Proc. SPIE 8056, Visual Information Processing XX, 80560J (3 June 2011); doi: 10.1117/12.887046
Show Author Affiliations
Mustafa Jaber, Rochester Institute of Technology (United States)
Eli Saber, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 8056:
Visual Information Processing XX
Zia-ur Rahman; Stephen E. Reichenbach; Mark Allen Neifeld, Editor(s)

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