Multi-scale discriminant saliency with wavelet-based Hidden Markov Tree modelling

Anh Cat Le Ngo; Li-Minn Ang; Jasmine Kah Phooi Seng; Guoping Qiu

doi:10.1016/j.compeleceng.2014.01.012

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Multi-scale discriminant saliency with wavelet-based Hidden Markov Tree modelling

Journal article

Peer reviewed

Multi-scale discriminant saliency with wavelet-based Hidden Markov Tree modelling

Anh Cat Le Ngo, Li-Minn Ang, Jasmine Kah Phooi Seng and Guoping Qiu

Computers & Electrical Engineering, Vol.40(4), pp.1376-1389

2014

DOI: https://doi.org/10.1016/j.compeleceng.2014.01.012

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Abstract

Binary classification

Discriminant power

Expectation Maximization

Hidden Markov tree

Maximum A posterior probabilities

Quadtree structure

Qualitative assessments

Quantitative tool

Hidden Markov models

Supposed saliency is a binary classification between centre and surround classes, saliency value is measured as their discriminant power. As the features are defined by sizes of chosen windows, a saliency value at each location is varied accordingly. This paper proposes computing saliency as discriminant power in multiple dyadic scales of Wavelet Hidden Markov Tree (HMT), in which two consecutive dyadic scales provide surrounding and central features, organized in a quad-tree structure. Their discriminant power is estimated as maximum a posterior probability (MAP) by Expectation-Maximization (EM) iterations. Then, a final saliency value is the maximum discriminant power generated among these scales. Standard quantitative tools and qualitative assessments are used for evaluating the proposed multi-scale discriminant saliency (MDIS) against the well-know information based approach AIM on its image collection with eye-tracking data. Simulation results are presented and analysed to verify the validity of MDIS as well as point out its limitation for further research direction.

Details

Title: Multi-scale discriminant saliency with wavelet-based Hidden Markov Tree modelling
Authors: Anh Cat Le Ngo (Author) - University of Nottingham Malaysia Campus
Li-Minn Ang (Author) - Edith Cowan University
Jasmine Kah Phooi Seng (Author) - Edith Cowan University
Guoping Qiu (Author) - University of Nottingham
Publication details: Computers & Electrical Engineering, Vol.40(4), pp.1376-1389
Publisher: Elsevier Ltd
Date published: 2014
DOI: 10.1016/j.compeleceng.2014.01.012
ISSN: 0045-7906; 1879-0755; 0045-7906
Organisation Unit: University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
Language: English
Record Identifier: 99513889602621
Output Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic