• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Visual Saliency Detection Based on color Frequency Features under Bayesian framework


Abstract

Saliency detection in neurobiology is a vehement research during the last few years, several cognitive and interactive systems are designed to simulate saliency model (an attentional mechanism, which focuses on the worthiest part in the image). In this paper, a bottom up saliency detection model is proposed by taking into account the color and luminance frequency features of RGB, CIE L*a*b* color space of the image. We employ low-level features of image and apply band pass filter to estimate and highlight salient region. We compute the likelihood probability by applying Bayesian framework at pixels. Experiments on two publically available datasets (MSRA and SED2) show that our saliency model performs better as compared to the ten state of the art algorithms by achieving higher precision, better recall and F-Measure.


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Cite this article

[IEEE Style]
Naeem Ayoub, Zhenguo Gao, Danjie Chen, Rachida Tobji and Nianmin Yao, "Visual Saliency Detection Based on color Frequency Features under Bayesian framework," KSII Transactions on Internet and Information Systems, vol. 12, no. 2, pp. 676-692, 2018. DOI: 10.3837/tiis.2018.02.008

[ACM Style]
Ayoub, N., Gao, Z., Chen, D., Tobji, R., and Yao, N. 2018. Visual Saliency Detection Based on color Frequency Features under Bayesian framework. KSII Transactions on Internet and Information Systems, 12, 2, (2018), 676-692. DOI: 10.3837/tiis.2018.02.008