NDLI: Segmentation-enhanced saliency detection model based on distance transform and center bias

Content Provider	IEEE Xplore Digital Library
Author	Hong-Yun Gao Kin-Man Lam
Copyright Year	2014
Description	Author affiliation: Dept. of Electron. & Inf. Eng, Hong Kong Polytech. Univ., Hong Kong, China (Hong-Yun Gao; Kin-Man Lam)
Abstract	Saliency detection is one of the extraordinary abilities of the human visual system (HVS), and also provides a powerful tool for predicting where humans tend to focus in the free-viewing process. In this paper, we propose a novel method for computing image saliency. At first, an image is subject to $L_{0}$ smoothing to characterize its fundamental constituents while diminishing insignificant details. Distance-transform-based saliency detection is then applied to the smoothed image, to extract the general salient regions and form a rough saliency map. Next, the segmentation information generated by normalized cuts is used to improve the saliency detection performance by averaging the saliency values in each segmented block. Finally, we employ the center-bias mechanism to further improve the saliency model. The proposed method is compared with six existing saliency models, and achieves the best performance in terms of the area under the ROC curve (AUC).
Sponsorship	IEEE Signal Process. Soc.
Starting Page	2803
Ending Page	2807
File Size	656695
Page Count	5
File Format	PDF
ISBN	9781479928934
DOI	10.1109/ICASSP.2014.6854111
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Transforms Smoothing methods Image edge detection Visualization Computational modeling Conferences image segmentation Saliency detection L0 smoothing filter distance transform center bias
Content Type	Text
Resource Type	Article

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