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Proceedings Paper

A Bayesian network-based approach for identifying regions of interest utilizing global image features
Author(s): Mustafa Jaber; Eli Saber
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Paper Abstract

An image-understanding algorithm for identifying Regions-of-Interest (ROI) in digital images is proposed. Global and regional features that characterize relations between image segments are fused in a probabilistic framework to generate ROI for an arbitrary image. Features are introduced as maps for spatial position, weighted similarity, and weighted homogeneity for image regions. The proposed methodology includes modules for image segmentation, feature extraction, and probabilistic reasoning. It differs from prior art by using machine learning techniques to discover the optimum Bayesian Network structure and probabilistic inference. It also eliminates the necessity for semantic understanding at intermediate stages. Experimental results show a competitive performance in comparison with the state-of- the-art techniques with an accuracy rate of ~80% on a set of ~20,000 publicly available color images. Applications of the proposed algorithm include content-based image retrieval, image indexing, automatic image annotation, mobile phone imagery, and digital photo cropping.

Paper Details

Date Published: 8 September 2010
PDF: 8 pages
Proc. SPIE 7798, Applications of Digital Image Processing XXXIII, 77980Q (8 September 2010); doi: 10.1117/12.859274
Show Author Affiliations
Mustafa Jaber, Rochester Institute of Technology (United States)
Eli Saber, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 7798:
Applications of Digital Image Processing XXXIII
Andrew G. Tescher, Editor(s)

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