@inproceedings { , title = {A low-complexity wavelet-based visual saliency model to predict fixations.}, 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.}, conference = {27th Institute of Electrical and Electronic Engineers (IEEE) International conference on electronics, circuits and systems 2020 (ICECS 2020)}, doi = {10.1109/ICECS49266.2020.9294905}, note = {INFO COMPLETE (Now published, checked and updated, info via Scopus 28/1/2021 LM) PERMISSION GRANTED (version = AAM; embargo = none; licence = BY-NC; POLICY = https://www.ieee.org/publications/rights/rights-policies.html 28/1/2021 LM) DOCUMENT READY (AAM rec'd from contact 28/1/2021 LM) ADDITIONAL INFO - Contact: MANJULA NARAWANASWAMY; Yafan Zhao; Wai Keung Fung; Nazila Fough (Set Statement - © 20XX 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.)}, publicationstatus = {Published}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, url = {https://rgu-repository.worktribe.com/output/1148904}, keyword = {Instrumentation & Sensors, Visual saliency model, Fixation prediction, Discrete wavelet transform, Image entropy}, year = {2020}, author = {Narayanaswamy, Manjula and Zhao, Yafan and Fung, Wai Keung and Fough, Nazila} }