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Inferring Visual Perceptual Object by Adaptive Fusion of Image Salient Features
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-08-27
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Xu, Xin& Mu, Nan& Zhang, Hong. 2015. Inferring Visual Perceptual Object by Adaptive Fusion of Image Salient Features. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1075226
Modern Language Association (MLA)
Xu, Xin…[et al.]. Inferring Visual Perceptual Object by Adaptive Fusion of Image Salient Features. Mathematical Problems in Engineering No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1075226
American Medical Association (AMA)
Xu, Xin& Mu, Nan& Zhang, Hong. Inferring Visual Perceptual Object by Adaptive Fusion of Image Salient Features. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1075226
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1075226