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.
Highlights
IntroductionIt is well-known that humans cannot observe every detail on an entire scene at first glance
It is well-known that humans cannot observe every detail on an entire scene at first glance.The human visual system focuses its attention on certain regions of a given scene according to their saliency
Exploiting the main advantage of rough set theory, which is that it does not need any additional information about the data [39], we propose a combination of four different state-of-the-art methods as feature descriptors included in the rules: Saliency Filter (SF) [23], Minimum Barrier Salient Object Detection (MBS) [40], Region-based Contrast (RC) [22], and Minimum Directional Contrast (MDC) [25]
Summary
It is well-known that humans cannot observe every detail on an entire scene at first glance. The human visual system focuses its attention on certain regions of a given scene according to their saliency. Koch et al [1] defined saliency as the extent to which an object stands out from its surrounding regions. Visual saliency detection systems aim at identifying the salient regions from a given image, and it is a fundamental task that has been addressed in recent years. Among the proposals for visual saliency detection, we identify two main approaches: fixation prediction and salient object detection.
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