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Main subject detection via adaptive feature refinement

[+] Author Affiliations
Cuong Vu

Oklahoma State University, Laboratory of Computational Perception and Image Quality, School of Electrical and Computer Engineering, Stillwater, Oklahoma 74078

Damon Chandler

Oklahoma State University, Laboratory of Computational Perception and Image Quality, School of Electrical and Computer Engineering, Stillwater, Oklahoma 74078

J. Electron. Imaging. 20(1), 013011 (March 10, 2011). doi:10.1117/1.3549884
History: Received April 29, 2010; Revised December 29, 2010; Accepted January 03, 2011; Published March 10, 2011; Online March 10, 2011
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Main subject detection (MSD) refers to the task of determining which spatial regions in an image correspond to the most visually relevant or scene-defining object(s) for general viewing purposes. This task, while trivial for a human, remains extremely challenging for a computer. Here, we present an algorithm for MSD which operates by adaptively refining low-level features. The algorithm computes, in a block-based fashion, five feature maps corresponding to lightness distance, color distance, contrast, local sharpness, and edge strength. These feature maps are adaptively combined and gradually refined via three stages. The final combination of the refined feature maps produces an estimate of the main subject's location. We tested the proposed algorithm on two extensive image databases. Our results show that relatively simple, low-level features, when used in an adaptive and iterative fashion, can be very effective at MSD.

© 2011 SPIE and IS&T

Citation

Cuong Vu and Damon Chandler
"Main subject detection via adaptive feature refinement", J. Electron. Imaging. 20(1), 013011 (March 10, 2011). ; http://dx.doi.org/10.1117/1.3549884


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