Saliency Detection via the Improved Hierarchical Principal Component Analysis Method
Joint Authors
Chammout, Anwar
Ghabally, Mike
Masri, Christina
Tantak, Nour
Ward, Fadi
Omar, Ghefar
Gennatas, Constantine
Chen, Yuantao
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-05
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
Aiming at the problems of intensive background noise, low accuracy, and high computational complexity of the current significant object detection methods, the visual saliency detection algorithm based on Hierarchical Principal Component Analysis (HPCA) has been proposed in the paper.
Firstly, the original RGB image has been converted to a grayscale image, and the original grayscale image has been divided into eight layers by the bit surface stratification technique.
Each image layer contains significant object information matching the layer image features.
Secondly, taking the color structure of the original image as the reference image, the grayscale image is reassigned by the grayscale color conversion method, so that the layered image not only reflects the original structural features but also effectively preserves the color feature of the original image.
Thirdly, the Principal Component Analysis (PCA) has been performed on the layered image to obtain the structural difference characteristics and color difference characteristics of each layer of the image in the principal component direction.
Fourthly, two features are integrated to get the saliency map with high robustness and to further refine our results; the known priors have been incorporated on image organization, which can place the subject of the photograph near the center of the image.
Finally, the entropy calculation has been used to determine the optimal image from the layered saliency map; the optimal map has the least background information and most prominently saliency objects than others.
The object detection results of the proposed model are closer to the ground truth and take advantages of performance parameters including precision rate (PRE), recall rate (REC), and F-measure (FME).
The HPCA model’s conclusion can obviously reduce the interference of redundant information and effectively separate the saliency object from the background.
At the same time, it had more improved detection accuracy than others.
American Psychological Association (APA)
Chen, Yuantao& Gennatas, Constantine& Omar, Ghefar& Ward, Fadi& Tantak, Nour& Masri, Christina…[et al.]. 2020. Saliency Detection via the Improved Hierarchical Principal Component Analysis Method. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1214609
Modern Language Association (MLA)
Chen, Yuantao…[et al.]. Saliency Detection via the Improved Hierarchical Principal Component Analysis Method. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1214609
American Medical Association (AMA)
Chen, Yuantao& Gennatas, Constantine& Omar, Ghefar& Ward, Fadi& Tantak, Nour& Masri, Christina…[et al.]. Saliency Detection via the Improved Hierarchical Principal Component Analysis Method. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1214609
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214609