A Novel Approach for Saliency Detection by using Stationary Wavelet Transform Low Level Features

A Novel Approach for Saliency Detection by using Stationary Wavelet Transform Low Level Features

Mulagundla Mahalaxmi

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Mr K. Durga prasad

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Dr. K. Manjunathachari

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Dr. M.N. Giri prasad

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Vardhaman College of Engineering

A Novel Approach for Saliency Detection by using Stationary Wavelet Transform Low Level Features

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Abstract

The ability of the Human Visual System (HVS) to detect an object in an image is extremely fast and reliable but how can a machine vision system detects the salient regions? many algorithms have been proposed to solve this problem by extracting features in either spatial or spectral domain, in this paper, A novel saliency detection model is introduced by utilizing low level features obtained from Stationary Wavelet Transform domain. Here Stationary Wavelet Transform (SWT) is preferred as the wavelet transform than Discrete Wavelet Transform (DWT), Since DWT is not a time-invariant transform. So to make it translation invariant SWT is introduced. And also unlike the other wavelet transforms SWT does not require down sampling, So image size is same as original even after decomposition, thus there is no information loss in respective sub bands. Experimental results demonstrate that proposed model produces better performance by using SWT than by using DWT with the overall F-Measure value being high.

References

20 Cites in Article

Reference Format

Unknown Title.
N Bruce,J Tsotsos (2005). Attention based on information maximization.
L Itti (1998). Model of saliency-based visual attention for rapid scene analysis.
A Oliva,A Torralba,M Castelhano,J Henderson (2003). Top-down control of visual attention in object detection.
A Oliva,A Torralba (2001). Modeling the shape of the scene: A holistic representation of the spatial envelope.
X Hou,L Zhang (2007). Saliency detection: A spectral residual approach.
R Merry (2005). Wavelet Theory and Application: A Literature Study.
N Murray (2011). Saliency estimation using a non-parametric low-level vision model.
Nevrez Imamoglu,Weisi Lin,Yuming Fang (2013). A Saliency Detection Model Using Low-Level Features Based on Wavelet Transform.
J Fowler (2004). The redundant discrete wavelet transform and additive noise.
G Nason,B Silverman (1995). The Stationary Wavelet Transform and Some Statistical Applications.
Y Kocyigit Andm,Korurek (2005). EMG signal classification using wavelet transform and fuzzy logic classifier.
John,L Semmlow (2004). Biosignal and Medical Image Processing.
Sergios Theodoridis,Konstantinos Koutroumbas (2009). Introduction.
S Goferman,L Zelnik-Manor,A Tal (2010). Context-aware saliency detection.
Tie Liu,Jian Sun,Nan-Ning Zheng,Xiaoou Tang,Heung-Yeung Shum (2007). Learning to Detect A Salient Object.
G Treisman,Gelade (1980). A featureintegration theory of attention.
S Frintrop (2005). VOCUS: A visual attention system for object detection and goal directed search.
L Itti (2000). On the Time Course of Top-Down and Bottom-Up Control of Visual Attention.
Jeremy Wolfe,Serena Butcher,Carol Lee,Megan Hyle (2003). Changing your mind: On the contributions of top-down and bottom-up guidance in visual search for feature singletons..

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Mulagundla Mahalaxmi. 2015. \u201cA Novel Approach for Saliency Detection by using Stationary Wavelet Transform Low Level Features\u201d. Global Journal of Research in Engineering - J: General Engineering GJRE-J Volume 14 (GJRE Volume 14 Issue J6).

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GJRE Volume 14 Issue J6

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Journal Specifications

Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

Keywords

feature map human visual system (HVS)saliency detection saliency map stationary wavelet transform

Submission ReceivedDecember 13, 2013
Peer Review Double Blind
Handling Editor
Accepted January 3, 2014
Published January 15, 2014

Version of record

v1.2

Issue date

January 6, 2015

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Article Matrices

Total Score: 114

Country: India

Subject: Global Journal of Research in Engineering - J: General Engineering

Authors: Mulagundla Mahalaxmi, Mr K. Durga prasad, Dr. K. Manjunathachari, Dr. M.N. Giri prasad (PhD/Dr. count: 2)

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Publish Date: 2015 01, Tue

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Total Views: 4381

Total Downloads: 2283

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