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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 973241, 9 pages
Research Article

Inferring Visual Perceptual Object by Adaptive Fusion of Image Salient Features

Xin Xu,1,2 Nan Mu,1,2 and Hong Zhang1,2

1School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430081, China
2Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan 430081, China

Received 19 September 2014; Revised 25 January 2015; Accepted 7 February 2015

Academic Editor: Marco Pérez-Cisneros

Copyright © 2015 Xin Xu 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 computational model with active environment perception can be useful for many applications including image retrieval, object recognition, and image segmentation. Previous work on bottom-up saliency computation typically relies on hand-crafted low-level image features. However, the adaptation of saliency computational model towards different kinds of scenes remains a challenge. For a low-level image feature, it can contribute greatly on some images but may be detrimental for saliency computation on other images. In this work, a novel data driven approach is proposed to adaptively select proper features for different kinds of images. This method exploits low-level features containing the most distinguishable salient information per image. Then the image saliency can be calculated based on the adaptive weight selection scheme. A large number of experiments are conducted on the MSRA database to compare the performance of the proposed method with the state-of-the-art saliency computational models.