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A Low-complexity Wavelet-based Visual Saliency Model to Predict Fixations

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Author
Narayanaswamy, Manjula
Zhao, Yafan
Fung, Wai Keung
Fough, Nazila
Date
2020-12-28
Acceptance date
2020
Type
Conference paper
Publisher
IEEE
Embargoed until
2022-12-28
Metadata
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Abstract
A low-complexity wavelet-based visual saliency model to predict the regions of human eye fixations in images using low-level features is proposed. Unlike the existing wavelet-based saliency detection models, the proposed model requires only two channels - luminance (Y) and chrominance (Cr) in YCbCr colour space for saliency computation. These two channels are decomposed to their lowest resolution using Discrete Wavelet Transform (DWT) to extract local contrast features at multiple scales. These features are integrated at multiple levels using 2D entropy based combination scheme to derive a combined map. The combined map is normalised and enhanced using natural logarithm transformation to derive a final saliency map. The experimental results show that the proposed model has achieved better prediction accuracy with significant complexity reduction compared to the existing benchmark models over two large public image datasets.
Journal/conference proceeding
2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS 2020);
Citation
M. Narayanaswamy, Y. Zhao, W. K. Fung and N. Fough (2020) "A Low-complexity Wavelet-based Visual Saliency Model to Predict Fixations," 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Glasgow, Scotland, UK, 2020, pp. 1-4, doi: 10.1109/ICECS49266.2020.9294905.
URI
http://hdl.handle.net/10369/11326
DOI
https://doi.org/10.1109/ICECS49266.2020.9294905
Description
Conference paper published in proceedings of 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS) available at https://doi.org/10.1109/ICECS49266.2020.9294905
Rights
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsorship
Cardiff Metropolitan University (Grant ID: Cardiff Metropolian (Internal))
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  • School of Technologies Research [170]

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