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

journal contribution
posted on 2014-01-01, 00:00 authored by Anh Cat Le Ngo, K L M Ang, J K P Seng, G Qiu
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. © 2013 Elsevier Ltd. All rights reserved.

History

Journal

Computers and electrical engineering

Volume

40

Issue

4

Pagination

1376 - 1389

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

0045-7906

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2014, Elsevier

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