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Advances in Multimedia
Volume 2015, Article ID 625915, 11 pages
Research Article

An Improved Saliency Detection Approach for Flying Apsaras in the Dunhuang Grotto Murals, China

1Wuhan University of Technology, School of Computer Science and Technology, 122 Luoshi Road, Wuhan 430070, China
2Wuhan University, State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, 129 Luoyu Road, Wuhan 430079, China
3Wuhan University, Engineering Research Center for Spatio-Temporal Data Smart Acquisition and Application, Ministry of Education of China, 129 Luoyu Road, Wuhan 430079, China

Received 20 September 2014; Revised 5 January 2015; Accepted 23 January 2015

Academic Editor: Chong Wah Ngo

Copyright © 2015 Zhong Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Saliency can be described as the ability of an item to be detected from its background in any particular scene, and it aims to estimate the probable location of the salient objects. Due to the salient map that computed by local contrast features can extract and highlight the edge parts including painting lines of Flying Apsaras, in this paper, we proposed an improved approach based on a frequency-tuned method for visual saliency detection of Flying Apsaras in the Dunhuang Grotto Murals, China. This improved saliency detection approach comprises three important steps: (1) image color and gray channel decomposition; (2) gray feature value computation and color channel convolution; (3) visual saliency definition based on normalization of previous visual saliency and spatial attention function. Unlike existing approaches that rely on many complex image features, this proposed approach only used local contrast and spatial attention information to simulate human’s visual attention stimuli. This improved approach resulted in a much more efficient salient map in the aspect of computing performance. Furthermore, experimental results on the dataset of Flying Apsaras in the Dunhuang Grotto Murals showed that the proposed visual saliency detection approach is very effective when compared with five other state-of-the-art approaches.