Image understanding algorithms for segmentation evaluation and region-of-interest identification using Bayesian networks

Mustafa Jaber; Eli Saber

doi:10.1117/12.887046

3 June 2011 Image understanding algorithms for segmentation evaluation and region-of-interest identification using Bayesian networks

Mustafa Jaber, Eli Saber

Author Affiliations +

Proceedings Volume 8056, Visual Information Processing XX; 80560J (2011) https://doi.org/10.1117/12.887046
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

Mustafa Jaber and Eli Saber "Image understanding algorithms for segmentation evaluation and region-of-interest identification using Bayesian networks", Proc. SPIE 8056, Visual Information Processing XX, 80560J (3 June 2011); https://doi.org/10.1117/12.887046

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