Visual Saliency Detection Using a Rule-Based Aggregation Approach

被引:6
|
作者
Lopez-Alanis, Alberto [1 ]
Lizarraga-Morales, Rocio A. [2 ]
Sanchez-Yanez, Raul E. [1 ]
Martinez-Rodriguez, Diana E. [1 ]
Contreras-Cruz, Marco A. [1 ]
机构
[1] Univ Guanajuato, Dept Ingn Elect, DICIS, Salamanca 36885, Mexico
[2] Univ Guanajuato, Dept Arte & Empresa, DICIS, Salamanca 36885, Mexico
来源
APPLIED SCIENCES-BASEL | 2019年 / 9卷 / 10期
关键词
binary saliency estimation; rough-set-based rules; saliency detection; OBJECT DETECTION; MODEL; ATTENTION;
D O I
10.3390/app9102015
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
引用
收藏
页数:18
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