## 2 Related Works  
According to the objective and the technical component of this paper, we broadly reviewed previous academic work related to several research areas: salient region detection, saliency cuts, and tactile graphic generation. Salient region detection is further extended to include objectless estimation in object recognition. Both are important and benefit different applications in high-level scene analysis [21]. Saliency models based on bottom-up methods convert natural scene images into saliency maps, where each pixel/superpixel or region is assigned a saliency value or probability. These methods initially apply image segmentation techniques (e.g., graph-based [22], mean shift [23], or superpixels [24]) to the input image and segment homogeneous regions to extract feature statistics from each segmented region to detect salient regions. [PERSON] et al. [17] demonstrated two saliency models: histogram-based contrast (HC), which assigns a pixel-wise saliency value, as well as region-based contrast (RC), which incorporates spatial relations at the cost of reduced computational efficiency. [PERSON] [25] proposed an object-based attention model that automatically identifies a series of regions far away from the image center as background prototypes. [PERSON] et al. [26] combined widely used contrast measurements, namely, center-surround, corner-surround, and global contrast, to detect visual saliency. [PERSON] et al. [27] developed a framework for saliency detection by first modeling the background and then separating salient objects from the background. Color contrast and spatial distribution were used to obtain pixel-accurate saliency maps. Another popular approach is saliency object detection methods based on deep convolutional neural networks (CNNs). Recently employed, these methods achieve substantially better results than traditional approaches because CNNs are typically pre-trained on datasets for visual recognition tasks. [PERSON] et al. [28] introduced the encoded low-level distance map (ELD-map), which directly encodes the feature distance between each pair of superpixels in an image. The encoded feature distance map has a strong discriminative power in evaluating similarities between different parts of an image with precise boundaries among superpixels. [PERSON] et al. [29] proposed a neural network architecture, which has fully connected layers on top of CNNs responsible for feature extraction at three different scales. In addition, they created a novel and challenging dataset, HKU-IS, for saliency model research and evaluation. [PERSON] et al. [30] demonstrated an end-to-end saliency detection model, the DNSNet, to detect salient objects with a new hierarchical refinement model, the HRCNN, which can refine saliency maps hierarchically and progressively to recover image details  
Figure 1: Example of the proposed method: (**a**) input image, (**b**) saliency map, (**c**) saliency cuts, (**d**) Salient objects. by integrating local context information without using over-segmentation methods. [PERSON] et al. [31] proposed a framework that performs both dense and sparse labeling (DSL) with multi-dimensional features for saliency detection. DSL consists of three major steps: dense labeling (DL), sparse labeling (SL), and deep convolutional (DC) networking. [PERSON] et al. [32] proposed a deep network, which consists of a fully convolutional stream at the pixel level and a spatial pooling stream at the segment level. A multi-scale fully convolutional network (MS-FCN) takes a raw image as input then directly produces a saliency map with pixel-level accuracy. In recent years, approaches using saliency cuts have been widely explored because these methods focus on providing a binary mask of salient objects with the aid of a saliency map. Saliency cuts automatically segment a salient object from the background. With a saliency map input and using the iterative GrabCuts algorithm [20], we can extract a precise image mask [16; 17; 18; 33] [PERSON] et al.