## 1 Introduction  
Human beings have an incredible ability to visually capture relevant targets quickly and accurately, which is called the focus of attention or saliency. Recognizing these conspicuous, or salient, ranges in visual fields empowers one to apportion restricted perceptual resources in a proficient way [1]. This visual saliency mechanism makes some objects in a scene stand out from the surroundings, a focus that raises plenty of research interest. In general, accurately detecting the most visually significant foreground object in a scene, referred to as salient object detection, may be goal-driven or stimulus-driven, corresponding to the top-down or bottom-up process in human visual perception, respectively. Bottom-up approaches are based on low-level image attributes such as color, intensities, gradient, edges, and boundaries, and are usually fast, involuntary, and more effective in detecting fine details rather than the information pertaining to the overall shape. In contrast, top-down saliencymodels are based on representative features from samples while being slow, task-driven, voluntary, and closed-loop. Recently, visual attention modeling has been extended to highlight objects uniformly and widely used as a first step in the computer vision field for its applications in image and video compression [2], image retargeting [3; 4; 5], image retrieval [6; 7], object-of-interest image segmentation [8], object recognition [9; 10; 11], image classification [12], and advertising evaluation [13]. The recent success of deep learning in object recognition and classification has brought about a revolution in computer vision [14]. Although significant progress has been made in the research area of visual attention, it remains not only one of the most important fields but also a highly challenging issue in image analysis, pattern recognition, and computer vision [15]. Salient object detection mainly consists of two phases: saliency map generation and saliency cuts. Saliency map generation concentrates on producing a pixel-level or region-level map, in which each pixel or region is assigned a value proportional to its saliency. Saliency cuts focus on providing a binary mask of a salient object. There have been a variety of saliency cuts methods proposed [16; 17], and these methods use global thresholding techniques for calculating a threshold value by disintegrating the pixels in a saliency map into the foreground and background regions according to their saliency values. The results of these methods are usually undermined by incorrect saliency maps as well as low saliency values for determining background regions in complex natural scene images. To increase the accuracy of a saliency map, some approaches use the original input image along with the saliency map in the binarization process [16; 17; 18]. Extracting salient objects from a scene and implementing tactile representation can assist the visually impaired to clearly understand important information contained in images. In general, the tactile representation of salient objects in a natural scene image should be designed as simple as possible and easily understood by the visually impaired. Moreover, a wide range of studies are being conducted to find optimal methods for extracting important objects from natural scene images [15]. Generally, tactile graphic information that visually impaired people can perceive by touching, is mainly obtained from the natural scene images through two steps: salient object extraction and tactile graphic translation. We focused on the automatic object segmentation approach for integrating local adaptive thresholding using integral images and the GrabCuts algorithm.