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Probabilistic approach for extracting regions of interest in digital images

[+] Author Affiliations
Mustafa I. Jaber

Rochester Institute of Technology, Chester F. Carlson Center for Imaging Science, 54 Lomb Memorial Drive, Rochester, New York 14623

Eli Saber

Rochester Institute of Technology, Department of Electrical and Microelectronic Engineering, 79 Lomb Memorial Drive, Rochester, New York 14623

J. Electron. Imaging. 19(2), 023019 (June 21, 2010). doi:10.1117/1.3455427
History: Received November 08, 2009; Revised May 05, 2010; Accepted May 07, 2010; Published June 21, 2010; Online June 21, 2010
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We propose an image-understanding algorithm for identifying and ranking regions of perceptually relevant content in digital images. Global features that characterize relations between image regions are fused in a probabilistic framework to generate a region ranking map (RRM) of an arbitrary image. Features are introduced as maps for spatial position, weighted similarity, and weighted homogeneity for image regions. Further analysis of the RRM, based on the receiver operating characteristic curve, has been utilized to generate a binary map that signifies region of interest in the test image. The algorithm 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 indicate an accuracy rate of 90% on a set of 4000 color images that are publicly available and compare favorably to state-of-the-art techniques. Applications of the proposed algorithm include smart image and document rendering, content-based image retrieval, adaptive image compression and coding, and automatic image annotation.

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Citation

Mustafa I. Jaber and Eli Saber
"Probabilistic approach for extracting regions of interest in digital images", J. Electron. Imaging. 19(2), 023019 (June 21, 2010). ; http://dx.doi.org/10.1117/1.3455427


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