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Proceedings Paper

A novel visual saliency analysis model based on dynamic multiple feature combination strategy
Author(s): Jing Lv; Qi Ye; Wen Lv; Libao Zhang
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Paper Abstract

The human visual system can quickly focus on a small number of salient objects. This process was known as visual saliency analysis and these salient objects are called focus of attention (FOA). The visual saliency analysis mechanism can be used to extract the salient regions and analyze saliency of object in an image, which is time-saving and can avoid unnecessary costs of computing resources. In this paper, a novel visual saliency analysis model based on dynamic multiple feature combination strategy is introduced. In the proposed model, we first generate multi-scale feature maps of intensity, color and orientation features using Gaussian pyramids and the center-surround difference. Then, we evaluate the contribution of all feature maps to the saliency map according to the area of salient regions and their average intensity, and attach different weights to different features according to their importance. Finally, we choose the largest salient region generated by the region growing method to perform the evaluation. Experimental results show that the proposed model cannot only achieve higher accuracy in saliency map computation compared with other traditional saliency analysis models, but also extract salient regions with arbitrary shapes, which is of great value for the image analysis and understanding.

Paper Details

Date Published: 26 June 2017
PDF: 7 pages
Proc. SPIE 10334, Automated Visual Inspection and Machine Vision II, 103340M (26 June 2017); doi: 10.1117/12.2270179
Show Author Affiliations
Jing Lv, North China Electric Power Univ. (China)
Qi Ye, Beijing Normal Univ. (China)
Wen Lv, Beijing Normal Univ. (China)
Libao Zhang, Beijing Normal Univ. (China)


Published in SPIE Proceedings Vol. 10334:
Automated Visual Inspection and Machine Vision II
Jürgen Beyerer; Fernando Puente León, Editor(s)

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