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Visual Saliency Detection Using a Rule-Based Aggregation Approach

1
Departamento de Ingeniería Electrónica, DICIS, Universidad de Guanajuato, Salamanca 36885, Mexico
2
Departamento Arte y Empresa, DICIS, Universidad de Guanajuato, Salamanca 36885, Mexico
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(10), 2015; https://doi.org/10.3390/app9102015
Received: 13 April 2019 / Revised: 9 May 2019 / Accepted: 9 May 2019 / Published: 16 May 2019
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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Abstract

In this paper, we propose an approach for salient pixel detection using a rule-based system. In our proposal, rules are automatically learned by combining four saliency models. The learned rules are utilized for the detection of pixels of the salient object in a visual scene. The proposed methodology consists of two main stages. Firstly, in the training stage, the knowledge extracted from outputs of four state-of-the-art saliency models is used to induce an ensemble of rough-set-based rules. Secondly, the induced rules are utilized by our system to determine, in a binary manner, the pixels corresponding to the salient object within a scene. Being independent of any threshold value, such a method eliminates any midway uncertainty and exempts us from performing a post-processing step as is required in most approaches to saliency detection. The experimental results on three datasets show that our method obtains stable and better results than state-of-the-art models. Moreover, it can be used as a pre-processing stage in computer vision-based applications in diverse areas such as robotics, image segmentation, marketing, and image compression. View Full-Text
Keywords: binary saliency estimation; rough-set-based rules; saliency detection binary saliency estimation; rough-set-based rules; saliency detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Lopez-Alanis, A.; Lizarraga-Morales, R.A.; Sanchez-Yanez, R.E.; Martinez-Rodriguez, D.E.; Contreras-Cruz, M.A. Visual Saliency Detection Using a Rule-Based Aggregation Approach. Appl. Sci. 2019, 9, 2015.

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